CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

PubMed

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks

PubMed Central

Li, Min; Li, Dongyan; Tang, Yu; Wang, Jianxin

2017-01-01

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster. PMID:28858211

Improving Family Forest Knowledge Transfer through Social Network Analysis

ERIC Educational Resources Information Center

Gorczyca, Erika L.; Lyons, Patrick W.; Leahy, Jessica E.; Johnson, Teresa R.; Straub, Crista L.

2012-01-01

To better engage Maine's family forest landowners our study used social network analysis: a computational social science method for identifying stakeholders, evaluating models of engagement, and targeting areas for enhanced partnerships. Interviews with researchers associated with a research center were conducted to identify how social network…

Causal network analysis of head and neck keloid tissue identifies potential master regulators.

PubMed

Garcia-Rodriguez, Laura; Jones, Lamont; Chen, Kang Mei; Datta, Indrani; Divine, George; Worsham, Maria J

2016-10-01

To generate novel insights and hypotheses in keloid development from potential master regulators. Prospective cohort. Six fresh keloid and six normal skin samples from 12 anonymous donors were used in a prospective cohort study. Genome-wide profiling was done previously on the cohort using the Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA). The 190 statistically significant CpG islands between keloid and normal tissue mapped to 152 genes (P < .05). The top 10 statistically significant genes (VAMP5, ACTR3C, GALNT3, KCNAB2, LRRC61, SCML4, SYNGR1, TNS1, PLEKHG5, PPP1R13-α, false discovery rate <.015) were uploaded into the Ingenuity Pathway Analysis software's Causal Network Analysis (QIAGEN, Redwood City, CA). To reflect expected gene expression direction in the context of methylation changes, the inverse of the methylation ratio from keloid versus normal tissue was used for the analysis. Causal Network Analysis identified disease-specific master regulator molecules based on downstream differentially expressed keloid-specific genes and expected directionality of expression (hypermethylated vs. hypomethylated). Causal Network Analysis software identified four hierarchical networks that included four master regulators (pyroxamide, tributyrin, PRKG2, and PENK) and 19 intermediate regulators. Causal Network Analysis of differentiated methylated gene data of keloid versus normal skin demonstrated four causal networks with four master regulators. These hierarchical networks suggest potential driver roles for their downstream keloid gene targets in the pathogenesis of the keloid phenotype, likely triggered due to perturbation/injury to normal tissue. NA Laryngoscope, 126:E319-E324, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Measuring Road Network Vulnerability with Sensitivity Analysis

PubMed Central

Jun-qiang, Leng; Long-hai, Yang; Liu, Wei-yi; Zhao, Lin

2017-01-01

This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery. PMID:28125706

Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

PubMed

Della Gatta, Giusy; Palomero, Teresa; Perez-Garcia, Arianne; Ambesi-Impiombato, Alberto; Bansal, Mukesh; Carpenter, Zachary W; De Keersmaecker, Kim; Sole, Xavier; Xu, Luyao; Paietta, Elisabeth; Racevskis, Janis; Wiernik, Peter H; Rowe, Jacob M; Meijerink, Jules P; Califano, Andrea; Ferrando, Adolfo A

2012-02-26

The TLX1 and TLX3 transcription factor oncogenes have a key role in the pathogenesis of T cell acute lymphoblastic leukemia (T-ALL). Here we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3. This systems biology analysis defined T cell leukemia homeobox 1 (TLX1) and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL. Notably, a network structure analysis of this hierarchical network identified RUNX1 as a key mediator of the T-ALL induced by TLX1 and TLX3 and predicted a tumor-suppressor role for RUNX1 in T cell transformation. Consistent with these results, we identified recurrent somatic loss-of-function mutations in RUNX1 in human T-ALL. Overall, these results place TLX1 and TLX3 at the top of an oncogenic transcriptional network controlling leukemia development, show the power of network analyses to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor-suppressor gene in T-ALL.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance

PubMed Central

Stringer, Clive; Beeknoo, Neeraj

2017-01-01

The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King’s College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A ‘core’ subnetwork containing only 13–17% of all edges channelled 83–90% of the patient flow, while an ‘ephemeral’ network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing. PMID:28968472

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

PubMed Central

Li, Wenyuan; Liu, Chun-Chi; Zhang, Tong; Li, Haifeng; Waterman, Michael S.; Zhou, Xianghong Jasmine

2011-01-01

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks. PMID:21698123

Neural network system and methods for analysis of organic materials and structures using spectral data

DOEpatents

Meyer, Bernd J.; Sellers, Jeffrey P.; Thomsen, Jan U.

1993-01-01

Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

Social Network Analysis: A Simple but Powerful Tool for Identifying Teacher Leaders

ERIC Educational Resources Information Center

Smith, P. Sean; Trygstad, Peggy J.; Hayes, Meredith L.

2018-01-01

Instructional teacher leadership is central to a vision of distributed leadership. However, identifying instructional teacher leaders can be a daunting task, particularly for administrators who find themselves either newly appointed or faced with high staff turnover. This article describes the use of social network analysis (SNA), a simple but…

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

Computational Framework for Analysis of Prey–Prey Associations in Interaction Proteomics Identifies Novel Human Protein–Protein Interactions and Networks

PubMed Central

Saha, Sudipto; Dazard, Jean-Eudes; Xu, Hua; Ewing, Rob M.

2013-01-01

Large-scale protein–protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait–prey and cocomplexed preys (prey–prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein–protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait–prey and prey–prey interactions can be used to refine network topology and extend known protein networks. PMID:22845868

Investigating Student Communities with Network Analysis of Interactions in a Physics Learning Center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; O'Brien, George

2009-11-01

We describe our initial efforts at implementing social network analysis to visualize and quantify student interactions in Florida International University's Physics Learning Center. Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at FIU. Our implementation of a research and learning community, embedded within a course reform effort, has led to increased recruitment and retention of physics majors. Finn and Rock [1997] link the academic and social integration of students to increased rates of retention. To identify these interactions, we have initiated an investigation that utilizes social network analysis to identify primary community participants. Community interactions are then characterized through the network's density and connectivity, shedding light on learning communities and participation. Preliminary results, further research questions, and future directions utilizing social network analysis are presented.

A graph-based network-vulnerability analysis system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Swiler, L.P.; Phillips, C.; Gaylor, T.

1998-05-03

This paper presents a graph based approach to network vulnerability analysis. The method is flexible, allowing analysis of attacks from both outside and inside the network. It can analyze risks to a specific network asset, or examine the universe of possible consequences following a successful attack. The analysis system requires as input a database of common attacks, broken into atomic steps, specific network configuration and topology information, and an attacker profile. The attack information is matched with the network configuration information and an attacker profile to create a superset attack graph. Nodes identify a stage of attack, for example themore » class of machines the attacker has accessed and the user privilege level he or she has compromised. The arcs in the attack graph represent attacks or stages of attacks. By assigning probabilities of success on the arcs or costs representing level of effort for the attacker, various graph algorithms such as shortest path algorithms can identify the attack paths with the highest probability of success.« less

A graph-based network-vulnerability analysis system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Swiler, L.P.; Phillips, C.; Gaylor, T.

1998-01-01

This report presents a graph-based approach to network vulnerability analysis. The method is flexible, allowing analysis of attacks from both outside and inside the network. It can analyze risks to a specific network asset, or examine the universe of possible consequences following a successful attack. The analysis system requires as input a database of common attacks, broken into atomic steps, specific network configuration and topology information, and an attacker profile. The attack information is matched with the network configuration information and an attacker profile to create a superset attack graph. Nodes identify a stage of attack, for example the classmore » of machines the attacker has accessed and the user privilege level he or she has compromised. The arcs in the attack graph represent attacks or stages of attacks. By assigning probabilities of success on the arcs or costs representing level-of-effort for the attacker, various graph algorithms such as shortest-path algorithms can identify the attack paths with the highest probability of success.« less

Ciência & Saúde Coletiva: scientific production analysis and collaborative research networks.

PubMed

Conner, Norma; Provedel, Attilio; Maciel, Ethel Leonor Noia

2017-03-01

The purpose of this metric and descriptive study was to identify the most productive authors and their collaborative research networks from articles published in Ciência & Saúde Coletiva between, 2005, and 2014. Authors meeting the cutoff criteria of at least 10 articles were considered the most productive authors. VOSviewer and Network Workbench technologies were applied for visual representations of collaborative research networks involving the most productive authors in the period. Initial analysis recovered 2511 distinct articles, with 8920 total authors with an average of 3.55 authors per article. Author analysis revealed 6288 distinct authors, 24 of these authors were identified as the most productive. These 24 authors generated 287 articles with an average of 4.31 authors per article, and represented 8 separate collaborative partnerships, the largest of which had 14 authors, indicating a significant degree of collaboration among these authors. This analysis provides a visual representation of networks of knowledge development in public health and demonstrates the usefulness of VOSviewer and Network Workbench technologies in future research.

An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges and Opportunities.

PubMed

Valente, Thomas W; Pitts, Stephanie R

2017-03-20

The use of social network theory and analysis methods as applied to public health has expanded greatly in the past decade, yielding a significant academic literature that spans almost every conceivable health issue. This review identifies several important theoretical challenges that confront the field but also provides opportunities for new research. These challenges include (a) measuring network influences, (b) identifying appropriate influence mechanisms, (c) the impact of social media and computerized communications, (d) the role of networks in evaluating public health interventions, and (e) ethics. Next steps for the field are outlined and the need for funding is emphasized. Recently developed network analysis techniques, technological innovations in communication, and changes in theoretical perspectives to include a focus on social and environmental behavioral influences have created opportunities for new theory and ever broader application of social networks to public health topics.

A Network Text Analysis of David Ayer's "Fury"

ERIC Educational Resources Information Center

Hunter, Starling David; Smith, Susan

2015-01-01

Network Text Analysis (NTA) involves the creation of networks of words and/or concepts from linguistic data. Its key insight is that the position of words and concepts in a text network provides vital clues to the central and underlying themes of the text as a whole. Recent research has relied on inductive approaches to identify these themes. In…

Detecting complexes from edge-weighted PPI networks via genes expression analysis.

PubMed

Zhang, Zehua; Song, Jian; Tang, Jijun; Xu, Xinying; Guo, Fei

2018-04-24

Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization. We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks. On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and S n values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, S n value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria.

Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers

PubMed Central

2013-01-01

Background Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer. Results We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis. Conclusions In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://compbio.cs.umn.edu/NetPathID/. PMID:23822816

Process-based network decomposition reveals backbone motif structure

PubMed Central

Wang, Guanyu; Du, Chenghang; Chen, Hao; Simha, Rahul; Rong, Yongwu; Xiao, Yi; Zeng, Chen

2010-01-01

A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated). PMID:20498084

Social network types among older Korean adults: Associations with subjective health.

PubMed

Sohn, Sung Yun; Joo, Won-Tak; Kim, Woo Jung; Kim, Se Joo; Youm, Yoosik; Kim, Hyeon Chang; Park, Yeong-Ran; Lee, Eun

2017-01-01

With population aging now a global phenomenon, the health of older adults is becoming an increasingly important issue. Because the Korean population is aging at an unprecedented rate, preparing for public health problems associated with old age is particularly salient in this country. As the physical and mental health of older adults is related to their social relationships, investigating the social networks of older adults and their relationship to health status is important for establishing public health policies. The aims of this study were to identify social network types among older adults in South Korea and to examine the relationship of these social network types with self-rated health and depression. Data from the Korean Social Life, Health, and Aging Project were analyzed. Model-based clustering using finite normal mixture modeling was conducted to identify the social network types based on ten criterion variables of social relationships and activities: marital status, number of children, number of close relatives, number of friends, frequency of attendance at religious services, attendance at organized group meetings, in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality. Multivariate regression analysis was conducted to examine associations between the identified social network types and self-rated health and depression. The model-based clustering analysis revealed that social networks clustered into five types: diverse, family, congregant, congregant-restricted, and restricted. Diverse or family social network types were significantly associated with more favorable subjective mental health, whereas the restricted network type was significantly associated with poorer ratings of mental and physical health. In addition, our analysis identified unique social network types related to religious activities. In summary, we developed a comprehensive social network typology for older Korean adults. Copyright © 2016 Elsevier Ltd. All rights reserved.

Microarray analysis and scale-free gene networks identify candidate regulators in drought-stressed roots of loblolly pine (P. taeda L.)

PubMed Central

2011-01-01

Background Global transcriptional analysis of loblolly pine (Pinus taeda L.) is challenging due to limited molecular tools. PtGen2, a 26,496 feature cDNA microarray, was fabricated and used to assess drought-induced gene expression in loblolly pine propagule roots. Statistical analysis of differential expression and weighted gene correlation network analysis were used to identify drought-responsive genes and further characterize the molecular basis of drought tolerance in loblolly pine. Results Microarrays were used to interrogate root cDNA populations obtained from 12 genotype × treatment combinations (four genotypes, three watering regimes). Comparison of drought-stressed roots with roots from the control treatment identified 2445 genes displaying at least a 1.5-fold expression difference (false discovery rate = 0.01). Genes commonly associated with drought response in pine and other plant species, as well as a number of abiotic and biotic stress-related genes, were up-regulated in drought-stressed roots. Only 76 genes were identified as differentially expressed in drought-recovered roots, indicating that the transcript population can return to the pre-drought state within 48 hours. Gene correlation analysis predicts a scale-free network topology and identifies eleven co-expression modules that ranged in size from 34 to 938 members. Network topological parameters identified a number of central nodes (hubs) including those with significant homology (E-values ≤ 2 × 10-30) to 9-cis-epoxycarotenoid dioxygenase, zeatin O-glucosyltransferase, and ABA-responsive protein. Identified hubs also include genes that have been associated previously with osmotic stress, phytohormones, enzymes that detoxify reactive oxygen species, and several genes of unknown function. Conclusion PtGen2 was used to evaluate transcriptome responses in loblolly pine and was leveraged to identify 2445 differentially expressed genes responding to severe drought stress in roots. Many of the genes identified are known to be up-regulated in response to osmotic stress in pine and other plant species and encode proteins involved in both signal transduction and stress tolerance. Gene expression levels returned to control values within a 48-hour recovery period in all but 76 transcripts. Correlation network analysis indicates a scale-free network topology for the pine root transcriptome and identifies central nodes that may serve as drivers of drought-responsive transcriptome dynamics in the roots of loblolly pine. PMID:21609476

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

PubMed Central

Uddin, Raihan; Singh, Shiva M.

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning. PMID:29066959

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

PubMed

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Neural network system and methods for analysis of organic materials and structures using spectral data

DOEpatents

Meyer, B.J.; Sellers, J.P.; Thomsen, J.U.

1993-06-08

Apparatus and processes are described for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

NASA Astrophysics Data System (ADS)

Champeimont, Raphaël; Laine, Elodie; Hu, Shuang-Wei; Penin, Francois; Carbone, Alessandra

2016-05-01

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

Identification of interactive gene networks: a novel approach in gene array profiling of myometrial events during guinea pig pregnancy.

PubMed

Mason, Clifford W; Swaan, Peter W; Weiner, Carl P

2006-06-01

The transition from myometrial quiescence to activation is poorly understood, and the analysis of array data is limited by the available data mining tools. We applied functional analysis and logical operations along regulatory gene networks to identify molecular processes and pathways underlying quiescence and activation. We analyzed some 18,400 transcripts and variants in guinea pig myometrium at stages corresponding to quiescence and activation, and compared them to the nonpregnant (control) counterpart using a functional mapping tool, MetaCore (GeneGo, St Joseph, MI) to identify novel gene networks composed of biological pathways during mid (MP) and late (LP) pregnancy. Genes altered during quiescence and or activation were identified following gene specific comparisons with myometrium from nonpregnant animals, and then linked to curated pathways and formulated networks. The MP and LP networks were subtracted from each other to identify unique genomic events during those periods. For example, changes 2-fold or greater in genes mediating protein biosynthesis, programmed cell death, microtubule polymerization, and microtubule based movement were noted during the transition to LP. We describe a novel approach combining microarrays and genetic data to identify networks associated with normal myometrial events. The resulting insights help identify potential biomarkers and permit future targeted investigations of these pathways or networks to confirm or refute their importance.

Influence networks among substance abuse treatment clinics: implications for the dissemination of innovations.

PubMed

Johnson, Kimberly; Quanbeck, Andrew; Maus, Adam; Gustafson, David H; Dearing, James W

2015-09-01

Understanding influence networks among substance abuse treatment clinics may speed the diffusion of innovations. The purpose of this study was to describe influence networks in Massachusetts, Michigan, New York, Oregon, and Washington and test two expectations, using social network analysis: (1) Social network measures can identify influential clinics; and (2) Within a network, some weakly connected clinics access out-of-network sources of innovative evidence-based practices and can spread these innovations through the network. A survey of 201 clinics in a parent study on quality improvement provided the data. Network measures and sociograms were obtained from adjacency matrixes created by UCINet. We used regression analysis to determine whether network status relates to clinics' adopting innovations. Findings suggest that influential clinics can be identified and that loosely linked clinics were likely to join the study sooner than more influential clinics but were not more likely to have improved outcomes than other organizations. Findings identify the structure of influence networks for SUD treatment organizations and have mixed results on how those structures impacted diffusion of the intervention under study. Further study is necessary to test whether use of knowledge of the network structure will have an effect on the pace and breadth of dissemination of innovations.

A graph-based system for network-vulnerability analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Swiler, L.P.; Phillips, C.

1998-06-01

This paper presents a graph-based approach to network vulnerability analysis. The method is flexible, allowing analysis of attacks from both outside and inside the network. It can analyze risks to a specific network asset, or examine the universe of possible consequences following a successful attack. The graph-based tool can identify the set of attack paths that have a high probability of success (or a low effort cost) for the attacker. The system could be used to test the effectiveness of making configuration changes, implementing an intrusion detection system, etc. The analysis system requires as input a database of common attacks,more » broken into atomic steps, specific network configuration and topology information, and an attacker profile. The attack information is matched with the network configuration information and an attacker profile to create a superset attack graph. Nodes identify a stage of attack, for example the class of machines the attacker has accessed and the user privilege level he or she has compromised. The arcs in the attack graph represent attacks or stages of attacks. By assigning probabilities of success on the arcs or costs representing level-of-effort for the attacker, various graph algorithms such as shortest-path algorithms can identify the attack paths with the highest probability of success.« less

PhenomeExpress: a refined network analysis of expression datasets by inclusion of known disease phenotypes.

PubMed

Soul, Jamie; Hardingham, Timothy E; Boot-Handford, Raymond P; Schwartz, Jean-Marc

2015-01-29

We describe a new method, PhenomeExpress, for the analysis of transcriptomic datasets to identify pathogenic disease mechanisms. Our analysis method includes input from both protein-protein interaction and phenotype similarity networks. This introduces valuable information from disease relevant phenotypes, which aids the identification of sub-networks that are significantly enriched in differentially expressed genes and are related to the disease relevant phenotypes. This contrasts with many active sub-network detection methods, which rely solely on protein-protein interaction networks derived from compounded data of many unrelated biological conditions and which are therefore not specific to the context of the experiment. PhenomeExpress thus exploits readily available animal model and human disease phenotype information. It combines this prior evidence of disease phenotypes with the experimentally derived disease data sets to provide a more targeted analysis. Two case studies, in subchondral bone in osteoarthritis and in Pax5 in acute lymphoblastic leukaemia, demonstrate that PhenomeExpress identifies core disease pathways in both mouse and human disease expression datasets derived from different technologies. We also validate the approach by comparison to state-of-the-art active sub-network detection methods, which reveals how it may enhance the detection of molecular phenotypes and provide a more detailed context to those previously identified as possible candidates.

A Graph Oriented Approach for Network Forensic Analysis

ERIC Educational Resources Information Center

Wang, Wei

2010-01-01

Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex…

A systematic review of nurse-related social network analysis studies.

PubMed

Benton, D C; Pérez-Raya, F; Fernández-Fernández, M P; González-Jurado, M A

2015-09-01

Nurses frequently work as part of both uni- and multidisciplinary teams. Communication between team members is critical in the delivery of quality care. Social network analysis is increasingly being used to explore such communication. To explore the use of social network analysis involving nurses either as subjects of the study or as researchers. Standard systematic review procedures were applied to identify nurse-related studies that utilize social network analysis. A comparative thematic approach to synthesis was used. Both published and grey literature written in English, Spanish and Portuguese between January 1965 and December 2013 were identified via a structured search of CINAHL, SciELO and PubMed. In addition, Google and Yahoo search engines were used to identify additional grey literature using the same search strategy. Forty-three primary studies were identified with literature from North America dominating the published work. So far it would appear that no author or group of authors have developed a programme of research in the nursing field using the social network analysis approach although several authors may be in the process of doing so. The dominance of literature from North America may be viewed as problematic as the underlying structures and themes may be an artefact of cultural communication norms from this region. The use of social network analysis in relation to nursing and by nurse researchers has increased rapidly over the past two decades. The lack of longitudinal studies and the absence of replication across multiple sites should be seen as an opportunity for further research. This analytical approach is relatively new in the field of nursing but does show considerable promise in offering insights into the way information flows between individuals, teams, institutions and other structures. An understanding of these structures provides a means of improving communication. © 2014 International Council of Nurses.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

A meta-analysis of public microarray data identifies biological regulatory networks in Parkinson's disease.

PubMed

Su, Lining; Wang, Chunjie; Zheng, Chenqing; Wei, Huiping; Song, Xiaoqing

2018-04-13

Parkinson's disease (PD) is a long-term degenerative disease that is caused by environmental and genetic factors. The networks of genes and their regulators that control the progression and development of PD require further elucidation. We examine common differentially expressed genes (DEGs) from several PD blood and substantia nigra (SN) microarray datasets by meta-analysis. Further we screen the PD-specific genes from common DEGs using GCBI. Next, we used a series of bioinformatics software to analyze the miRNAs, lncRNAs and SNPs associated with the common PD-specific genes, and then identify the mTF-miRNA-gene-gTF network. Our results identified 36 common DEGs in PD blood studies and 17 common DEGs in PD SN studies, and five of the genes were previously known to be associated with PD. Further study of the regulatory miRNAs associated with the common PD-specific genes revealed 14 PD-specific miRNAs in our study. Analysis of the mTF-miRNA-gene-gTF network about PD-specific genes revealed two feed-forward loops: one involving the SPRK2 gene, hsa-miR-19a-3p and SPI1, and the second involving the SPRK2 gene, hsa-miR-17-3p and SPI. The long non-coding RNA (lncRNA)-mediated regulatory network identified lncRNAs associated with PD-specific genes and PD-specific miRNAs. Moreover, single nucleotide polymorphism (SNP) analysis of the PD-specific genes identified two significant SNPs, and SNP analysis of the neurodegenerative disease-specific genes identified seven significant SNPs. Most of these SNPs are present in the 3'-untranslated region of genes and are controlled by several miRNAs. Our study identified a total of 53 common DEGs in PD patients compared with healthy controls in blood and brain datasets and five of these genes were previously linked with PD. Regulatory network analysis identified PD-specific miRNAs, associated long non-coding RNA and feed-forward loops, which contribute to our understanding of the mechanisms underlying PD. The SNPs identified in our study can determine whether a genetic variant is associated with PD. Overall, these findings will help guide our study of the complex molecular mechanism of PD.

Identifying the Most Important 21st Century Workforce Competencies: An Analysis of the Occupational Information Network (O*NET). Research Report. ETS RR-13-21

ERIC Educational Resources Information Center

Burrus, Jeremy; Jackson, Teresa; Xi, Nuo; Steinberg, Jonathan

2013-01-01

To identify the most important competencies for college graduates to succeed in the 21st century workforce, we conducted an analysis of the Occupational Information Network (O*NET) database. O*NET is a large job analysis operated and maintained by the U.S. Department of Labor. We specifically analyzed ratings of the importance of abilities (52…

Meta-review of protein network regulating obesity between validated obesity candidate genes in the white adipose tissue of high-fat diet-induced obese C57BL/6J mice.

PubMed

Kim, Eunjung; Kim, Eun Jung; Seo, Seung-Won; Hur, Cheol-Goo; McGregor, Robin A; Choi, Myung-Sook

2014-01-01

Worldwide obesity and related comorbidities are increasing, but identifying new therapeutic targets remains a challenge. A plethora of microarray studies in diet-induced obesity models has provided large datasets of obesity associated genes. In this review, we describe an approach to examine the underlying molecular network regulating obesity, and we discuss interactions between obesity candidate genes. We conducted network analysis on functional protein-protein interactions associated with 25 obesity candidate genes identified in a literature-driven approach based on published microarray studies of diet-induced obesity. The obesity candidate genes were closely associated with lipid metabolism and inflammation. Peroxisome proliferator activated receptor gamma (Pparg) appeared to be a core obesity gene, and obesity candidate genes were highly interconnected, suggesting a coordinately regulated molecular network in adipose tissue. In conclusion, the current network analysis approach may help elucidate the underlying molecular network regulating obesity and identify anti-obesity targets for therapeutic intervention.

Analysis of bHLH coding genes using gene co-expression network approach.

PubMed

Srivastava, Swati; Sanchita; Singh, Garima; Singh, Noopur; Srivastava, Gaurava; Sharma, Ashok

2016-07-01

Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species.

An ANOVA approach for statistical comparisons of brain networks.

PubMed

Fraiman, Daniel; Fraiman, Ricardo

2018-03-16

The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

Reduced structural connectivity within a prefrontal-motor-subcortical network in amyotrophic lateral sclerosis.

PubMed

Buchanan, Colin R; Pettit, Lewis D; Storkey, Amos J; Abrahams, Sharon; Bastin, Mark E

2015-05-01

To investigate white matter structural connectivity changes associated with amyotrophic lateral sclerosis (ALS) using network analysis and compare the results with those obtained using standard voxel-based methods, specifically Tract-based Spatial Statistics (TBSS). MRI data were acquired from 30 patients with ALS and 30 age-matched healthy controls. For each subject, 85 grey matter regions (network nodes) were identified from high resolution structural MRI, and network connections formed from the white matter tracts generated by diffusion MRI and probabilistic tractography. Whole-brain networks were constructed using strong constraints on anatomical plausibility and a weighting reflecting tract-averaged fractional anisotropy (FA). Analysis using Network-based Statistics (NBS), without a priori selected regions, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls (P = 0.020). Reduced FA in three of the impaired network connections, which involved fibers of the corticospinal tract, correlated with rate of disease progression (P ≤ 0.024). A novel network-tract comparison revealed that the connections involved in the affected network had a strong correspondence (mean overlap of 86.2%) with white matter tracts identified as having reduced FA compared with the control group using TBSS. These findings suggest that white matter degeneration in ALS is strongly linked to the motor cortex, and that impaired structural networks identified using NBS have a strong correspondence to affected white matter tracts identified using more conventional voxel-based methods. © 2014 Wiley Periodicals, Inc.

Patent citation network in nanotechnology (1976-2004)

NASA Astrophysics Data System (ADS)

Li, Xin; Chen, Hsinchun; Huang, Zan; Roco, Mihail C.

2007-06-01

The patent citation networks are described using critical node, core network, and network topological analysis. The main objective is understanding of the knowledge transfer processes between technical fields, institutions and countries. This includes identifying key influential players and subfields, the knowledge transfer patterns among them, and the overall knowledge transfer efficiency. The proposed framework is applied to the field of nanoscale science and engineering (NSE), including the citation networks of patent documents, submitting institutions, technology fields, and countries. The NSE patents were identified by keywords "full-text" searching of patents at the United States Patent and Trademark Office (USPTO). The analysis shows that the United States is the most important citation center in NSE research. The institution citation network illustrates a more efficient knowledge transfer between institutions than a random network. The country citation network displays a knowledge transfer capability as efficient as a random network. The technology field citation network and the patent document citation network exhibit a␣less efficient knowledge diffusion capability than a random network. All four citation networks show a tendency to form local citation clusters.

Social network analysis in identifying influential webloggers: A preliminary study

NASA Astrophysics Data System (ADS)

Hasmuni, Noraini; Sulaiman, Nor Intan Saniah; Zaibidi, Nerda Zura

2014-12-01

In recent years, second generation of internet-based services such as weblog has become an effective communication tool to publish information on the Web. Weblogs have unique characteristics that deserve users' attention. Some of webloggers have seen weblogs as appropriate medium to initiate and expand business. These webloggers or also known as direct profit-oriented webloggers (DPOWs) communicate and share knowledge with each other through social interaction. However, survivability is the main issue among DPOW. Frequent communication with influential webloggers is one of the way to keep survive as DPOW. This paper aims to understand the network structure and identify influential webloggers within the network. Proper understanding of the network structure can assist us in knowing how the information is exchanged among members and enhance survivability among DPOW. 30 DPOW were involved in this study. Degree centrality and betweenness centrality measurement in Social Network Analysis (SNA) were used to examine the strength relation and identify influential webloggers within the network. Thus, webloggers with the highest value of these measurements are considered as the most influential webloggers in the network.

Patent Network Analysis and Quadratic Assignment Procedures to Identify the Convergence of Robot Technologies

PubMed Central

Lee, Woo Jin; Lee, Won Kyung

2016-01-01

Because of the remarkable developments in robotics in recent years, technological convergence has been active in this area. We focused on finding patterns of convergence within robot technology using network analysis of patents in both the USPTO and KIPO. To identify the variables that affect convergence, we used quadratic assignment procedures (QAP). From our analysis, we observed the patent network ecology related to convergence and found technologies that have great potential to converge with other robotics technologies. The results of our study are expected to contribute to setting up convergence based R&D policies for robotics, which can lead new innovation. PMID:27764196

Social network analysis identified central outcomes for core outcome sets using systematic reviews of HIV/AIDS.

PubMed

Saldanha, Ian J; Li, Tianjing; Yang, Cui; Ugarte-Gil, Cesar; Rutherford, George W; Dickersin, Kay

2016-02-01

Methods to develop core outcome sets, the minimum outcomes that should be measured in research in a topic area, vary. We applied social network analysis methods to understand outcome co-occurrence patterns in human immunodeficiency virus (HIV)/AIDS systematic reviews and identify outcomes central to the network of outcomes in HIV/AIDS. We examined all Cochrane reviews of HIV/AIDS as of June 2013. We defined a tie as two outcomes (nodes) co-occurring in ≥2 reviews. To identify central outcomes, we used normalized node betweenness centrality (nNBC) (the extent to which connections between other outcomes in a network rely on that outcome as an intermediary). We conducted a subgroup analysis by HIV/AIDS intervention type (i.e., clinical management, biomedical prevention, behavioral prevention, and health services). The 140 included reviews examined 1,140 outcomes, 294 of which were unique. The most central outcome overall was all-cause mortality (nNBC = 23.9). The most central and most frequent outcomes differed overall and within subgroups. For example, "adverse events (specified)" was among the most central but not among the most frequent outcomes, overall. Social network analysis methods are a novel application to identify central outcomes, which provides additional information potentially useful for developing core outcome sets. Copyright © 2016 Elsevier Inc. All rights reserved.

Social Network Analysis to Evaluate an Interdisciplinary Research Center

ERIC Educational Resources Information Center

Aboelela, Sally W.; Merrill, Jacqueline A.; Carley, Kathleen M.; Larson, Elaine

2007-01-01

We sought to examine the growth of an interdisciplinary center using social network analysis techniques. Specific aims were to examine the patterns of growth and interdisciplinary connectedness of the Center and to identify the social network characteristics of its productive members. The setting for this study was The Center for Interdisciplinary…

Topological Vulnerability Analysis

NASA Astrophysics Data System (ADS)

Jajodia, Sushil; Noel, Steven

Traditionally, network administrators rely on labor-intensive processes for tracking network configurations and vulnerabilities. This requires a great deal of expertise, and is error prone because of the complexity of networks and associated security data. The interdependencies of network vulnerabilities make traditional point-wise vulnerability analysis inadequate. We describe a Topological Vulnerability Analysis (TVA) approach that analyzes vulnerability dependencies and shows all possible attack paths into a network. From models of the network vulnerabilities and potential attacker exploits, we compute attack graphs that convey the impact of individual and combined vulnerabilities on overall security. TVA finds potential paths of vulnerability through a network, showing exactly how attackers may penetrate a network. From this, we identify key vulnerabilities and provide strategies for protection of critical network assets.

The Conundrum of Functional Brain Networks: Small-World Efficiency or Fractal Modularity

PubMed Central

Gallos, Lazaros K.; Sigman, Mariano; Makse, Hernán A.

2012-01-01

The human brain has been studied at multiple scales, from neurons, circuits, areas with well-defined anatomical and functional boundaries, to large-scale functional networks which mediate coherent cognition. In a recent work, we addressed the problem of the hierarchical organization in the brain through network analysis. Our analysis identified functional brain modules of fractal structure that were inter-connected in a small-world topology. Here, we provide more details on the use of network science tools to elaborate on this behavior. We indicate the importance of using percolation theory to highlight the modular character of the functional brain network. These modules present a fractal, self-similar topology, identified through fractal network methods. When we lower the threshold of correlations to include weaker ties, the network as a whole assumes a small-world character. These weak ties are organized precisely as predicted by theory maximizing information transfer with minimal wiring costs. PMID:22586406

Industrial entrepreneurial network: Structural and functional analysis

NASA Astrophysics Data System (ADS)

Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

2016-12-01

Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease.

PubMed

Modena, Brian D; Bleecker, Eugene R; Busse, William W; Erzurum, Serpil C; Gaston, Benjamin M; Jarjour, Nizar N; Meyers, Deborah A; Milosevic, Jadranka; Tedrow, John R; Wu, Wei; Kaminski, Naftali; Wenzel, Sally E

2017-06-01

Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals. Identify networks of genes reflective of underlying biological processes that define SA. Airway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. Weighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12-21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA. In this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes.

Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease

PubMed Central

Modena, Brian D.; Bleecker, Eugene R.; Busse, William W.; Erzurum, Serpil C.; Gaston, Benjamin M.; Jarjour, Nizar N.; Meyers, Deborah A.; Milosevic, Jadranka; Tedrow, John R.; Wu, Wei; Kaminski, Naftali

2017-01-01

Rationale: Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals. Objectives: Identify networks of genes reflective of underlying biological processes that define SA. Methods: Airway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. Measurements and Main Results: Weighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12–21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA. Conclusions: In this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes. PMID:27984699

Gene co-expression network analysis in Rhodobacter capsulatus and application to comparative expression analysis of Rhodobacter sphaeroides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pena-Castillo, Lourdes; Mercer, Ryan; Gurinovich, Anastasia

2014-08-28

The genus Rhodobacter contains purple nonsulfur bacteria found mostly in freshwater environments. Representative strains of two Rhodobacter species, R. capsulatus and R. sphaeroides, have had their genomes fully sequenced and both have been the subject of transcriptional profiling studies. Gene co-expression networks can be used to identify modules of genes with similar expression profiles. Functional analysis of gene modules can then associate co-expressed genes with biological pathways, and network statistics can determine the degree of module preservation in related networks. In this paper, we constructed an R. capsulatus gene co-expression network, performed functional analysis of identified gene modules, and investigatedmore » preservation of these modules in R. capsulatus proteomics data and in R. sphaeroides transcriptomics data. Results: The analysis identified 40 gene co-expression modules in R. capsulatus. Investigation of the module gene contents and expression profiles revealed patterns that were validated based on previous studies supporting the biological relevance of these modules. We identified two R. capsulatus gene modules preserved in the protein abundance data. We also identified several gene modules preserved between both Rhodobacter species, which indicate that these cellular processes are conserved between the species and are candidates for functional information transfer between species. Many gene modules were non-preserved, providing insight into processes that differentiate the two species. In addition, using Local Network Similarity (LNS), a recently proposed metric for expression divergence, we assessed the expression conservation of between-species pairs of orthologs, and within-species gene-protein expression profiles. Conclusions: Our analyses provide new sources of information for functional annotation in R. capsulatus because uncharacterized genes in modules are now connected with groups of genes that constitute a joint functional annotation. We identified R. capsulatus modules enriched with genes for ribosomal proteins, porphyrin and bacteriochlorophyll anabolism, and biosynthesis of secondary metabolites to be preserved in R. sphaeroides whereas modules related to RcGTA production and signalling showed lack of preservation in R. sphaeroides. In addition, we demonstrated that network statistics may also be applied within-species to identify congruence between mRNA expression and protein abundance data for which simple correlation measurements have previously had mixed results.« less

[Analysis of researchers' implication in a research-intervention in the Stork Network: a tool for institutional analysis].

PubMed

Fortuna, Cinira Magali; Mesquita, Luana Pinho de; Matumoto, Silvia; Monceau, Gilles

2016-09-19

This qualitative study is based on institutional analysis as the methodological theoretical reference with the objective of analyzing researchers' implication during a research-intervention and the interferences caused by this analysis. The study involved researchers from courses in medicine, nursing, and dentistry at two universities and workers from a Regional Health Department in follow-up on the implementation of the Stork Network in São Paulo State, Brazil. The researchers worked together in the intervention and in analysis workshops, supported by an external institutional analysis. Two institutions stood out in the analysis: the research, established mainly with characteristics of neutrality, and management, with Taylorist characteristics. Differences between researchers and difficulties in identifying actions proper to network management and research were some of the interferences that were identified. The study concludes that implication analysis is a powerful tool for such studies.

Visualization of e-Health Research Topics and Current Trends Using Social Network Analysis.

PubMed

Son, Youn-Jung; Jeong, Senator; Kang, Byeong-Gwon; Kim, Sun-Hyung; Lee, Soo-Kyoung

2015-05-01

E-health has been grown rapidly with significant impact on quality and safety of healthcare. However, there is a large gap between the postulated and empirically demonstrated benefits of e-health technologies and a need for a clearer mapping of its conceptual domains. Therefore, this study aimed to critically review the main research topics and trends of international e-health through social network analysis. Medical subject heading terms were used to retrieve 3,023 research articles published from 1979 through 2014 in the PubMed database. We extracted n-grams from the corpus using a text analysis program, generated co-occurrence networks, and then analyzed and visualized the networks using Pajek software. The hub and authority measures identified the most important research topics in e-health. Newly emerging topics by 4-year period units were identified as research trends. The most important research topics in e-health are personal health records (PHR), health information technology, primary care, mobile health, clinical decision support systems (CDSS), and so on. The eight groups obtained through ego network analysis can be divided into four semantically different areas, as follows: information technology, infrastructure, services, and subjects. Also, four historical trends in e-health research are identified: the first focusing on e-health and telemedicine; the second, PHR and monitoring; the third, CDSS and alert; and the fourth, mobile health and health literacy. This study promotes a systematic understanding of e-health by identifying topic networks, thereby contributing to the future direction of e-health research and education.

Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis.

PubMed

Rajamani, Deepa; Bhasin, Manoj K

2016-05-03

Pancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression. Multidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis. Rank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities. It is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.

Identifying and tracking attacks on networks: C3I displays and related technologies

NASA Astrophysics Data System (ADS)

Manes, Gavin W.; Dawkins, J.; Shenoi, Sujeet; Hale, John C.

2003-09-01

Converged network security is extremely challenging for several reasons; expanded system and technology perimeters, unexpected feature interaction, and complex interfaces all conspire to provide hackers with greater opportunities for compromising large networks. Preventive security services and architectures are essential, but in and of themselves do not eliminate all threat of compromise. Attack management systems mitigate this residual risk by facilitating incident detection, analysis and response. There are a wealth of attack detection and response tools for IP networks, but a dearth of such tools for wireless and public telephone networks. Moreover, methodologies and formalisms have yet to be identified that can yield a common model for vulnerabilities and attacks in converged networks. A comprehensive attack management system must coordinate detection tools for converged networks, derive fully-integrated attack and network models, perform vulnerability and multi-stage attack analysis, support large-scale attack visualization, and orchestrate strategic responses to cyber attacks that cross network boundaries. We present an architecture that embodies these principles for attack management. The attack management system described engages a suite of detection tools for various networking domains, feeding real-time attack data to a comprehensive modeling, analysis and visualization subsystem. The resulting early warning system not only provides network administrators with a heads-up cockpit display of their entire network, it also supports guided response and predictive capabilities for multi-stage attacks in converged networks.

Temporal Sequence of Hemispheric Network Activation during Semantic Processing: A Functional Network Connectivity Analysis

ERIC Educational Resources Information Center

Assaf, Michal; Jagannathan, Kanchana; Calhoun, Vince; Kraut, Michael; Hart, John, Jr.; Pearlson, Godfrey

2009-01-01

To explore the temporal sequence of, and the relationship between, the left and right hemispheres (LH and RH) during semantic memory (SM) processing we identified the neural networks involved in the performance of functional MRI semantic object retrieval task (SORT) using group independent component analysis (ICA) in 47 healthy individuals. SORT…

Quantifiable and objective approach to organizational performance enhancement.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholand, Andrew Joseph; Tausczik, Yla R.

This report describes a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to identify socially situated relationships between individuals which, though subtle, are highly influential. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships aremore » latent or unrecognized. This report outlines the philosophical antecedents of SLNA, the mechanics of preprocessing, processing, and post-processing stages, and some example results obtained by applying this approach to a 15-month corporate discussion archive.« less

Co-expression network analysis identified six hub genes in association with metastasis risk and prognosis in hepatocellular carcinoma

PubMed Central

Feng, Juerong; Zhou, Rui; Chang, Ying; Liu, Jing; Zhao, Qiu

2017-01-01

Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, and its carcinogenesis and progression are influenced by a complex network of gene interactions. A weighted gene co-expression network was constructed to identify gene modules associated with the clinical traits in HCC (n = 214). Among the 13 modules, high correlation was only found between the red module and metastasis risk (classified by the HCC metastasis gene signature) (R2 = −0.74). Moreover, in the red module, 34 network hub genes for metastasis risk were identified, six of which (ABAT, AGXT, ALDH6A1, CYP4A11, DAO and EHHADH) were also hub nodes in the protein-protein interaction network of the module genes. Thus, a total of six hub genes were identified. In validation, all hub genes showed a negative correlation with the four-stage HCC progression (P for trend < 0.05) in the test set. Furthermore, in the training set, HCC samples with any hub gene lowly expressed demonstrated a higher recurrence rate and poorer survival rate (hazard ratios with 95% confidence intervals > 1). RNA-sequencing data of 142 HCC samples showed consistent results in the prognosis. Gene set enrichment analysis (GSEA) demonstrated that in the samples with any hub gene highly expressed, a total of 24 functional gene sets were enriched, most of which focused on amino acid metabolism and oxidation. In conclusion, co-expression network analysis identified six hub genes in association with HCC metastasis risk and prognosis, which might improve the prognosis by influencing amino acid metabolism and oxidation. PMID:28430663

Mathematical analysis techniques for modeling the space network activities

NASA Technical Reports Server (NTRS)

Foster, Lisa M.

1992-01-01

The objective of the present work was to explore and identify mathematical analysis techniques, and in particular, the use of linear programming. This topic was then applied to the Tracking and Data Relay Satellite System (TDRSS) in order to understand the space network better. Finally, a small scale version of the system was modeled, variables were identified, data was gathered, and comparisons were made between actual and theoretical data.

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis[W

PubMed Central

Ma, Chuang; Xin, Mingming; Feldmann, Kenneth A.; Wang, Xiangfeng

2014-01-01

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive “noninformative” genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained “informative” genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes. PMID:24520154

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli.

PubMed

Antiqueira, Lucas; Janga, Sarath Chandra; Costa, Luciano da Fontoura

2012-11-01

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Social network types and well-being among South Korean older adults.

PubMed

Park, Sojung; Smith, Jacqui; Dunkle, Ruth E

2014-01-01

The social networks of older individuals reflect personal life history and cultural factors. Despite these two sources of variation, four similar network types have been identified in Europe, North America, Japan, and China: namely 'restricted', 'family', 'friend', and 'diverse'. This study identified the social network types of Korean older adults and examined differential associations of the network types with well-being. The analysis used data from the 2008 wave of the Korean Longitudinal Study of Aging (KLoSA: N = 4251, age range 65-108). We used a two-step cluster analytical approach to identify network types from seven indicators of network structure and function. Regression models determined associations between network types and well-being outcomes, including life satisfaction and depressive symptomatology. Cluster analysis of indicators of network structure and function revealed four types, including the restricted, friend, and diverse types. Instead of a family type, we found a couple-focused type. The young-old (age 65-74) were more likely to be in the couple-focused type and more of the oldest old (age 85+) belonged to the restricted type. Compared with the restricted network, older adults in all other networks were more likely to report higher life satisfaction and lower depressive symptomatology. Life course and cohort-related factors contribute to similarities across societies in network types and their associations with well-being. Korean-specific life course and socio-historical factors, however, may contribute to our unique findings about network types.

Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk.

PubMed

Kar, Siddhartha P; Tyrer, Jonathan P; Li, Qiyuan; Lawrenson, Kate; Aben, Katja K H; Anton-Culver, Hoda; Antonenkova, Natalia; Chenevix-Trench, Georgia; Baker, Helen; Bandera, Elisa V; Bean, Yukie T; Beckmann, Matthias W; Berchuck, Andrew; Bisogna, Maria; Bjørge, Line; Bogdanova, Natalia; Brinton, Louise; Brooks-Wilson, Angela; Butzow, Ralf; Campbell, Ian; Carty, Karen; Chang-Claude, Jenny; Chen, Yian Ann; Chen, Zhihua; Cook, Linda S; Cramer, Daniel; Cunningham, Julie M; Cybulski, Cezary; Dansonka-Mieszkowska, Agnieszka; Dennis, Joe; Dicks, Ed; Doherty, Jennifer A; Dörk, Thilo; du Bois, Andreas; Dürst, Matthias; Eccles, Diana; Easton, Douglas F; Edwards, Robert P; Ekici, Arif B; Fasching, Peter A; Fridley, Brooke L; Gao, Yu-Tang; Gentry-Maharaj, Aleksandra; Giles, Graham G; Glasspool, Rosalind; Goode, Ellen L; Goodman, Marc T; Grownwald, Jacek; Harrington, Patricia; Harter, Philipp; Hein, Alexander; Heitz, Florian; Hildebrandt, Michelle A T; Hillemanns, Peter; Hogdall, Estrid; Hogdall, Claus K; Hosono, Satoyo; Iversen, Edwin S; Jakubowska, Anna; Paul, James; Jensen, Allan; Ji, Bu-Tian; Karlan, Beth Y; Kjaer, Susanne K; Kelemen, Linda E; Kellar, Melissa; Kelley, Joseph; Kiemeney, Lambertus A; Krakstad, Camilla; Kupryjanczyk, Jolanta; Lambrechts, Diether; Lambrechts, Sandrina; Le, Nhu D; Lee, Alice W; Lele, Shashi; Leminen, Arto; Lester, Jenny; Levine, Douglas A; Liang, Dong; Lissowska, Jolanta; Lu, Karen; Lubinski, Jan; Lundvall, Lene; Massuger, Leon; Matsuo, Keitaro; McGuire, Valerie; McLaughlin, John R; McNeish, Iain A; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B; Narod, Steven A; Nedergaard, Lotte; Ness, Roberta B; Nevanlinna, Heli; Odunsi, Kunle; Olson, Sara H; Orlow, Irene; Orsulic, Sandra; Weber, Rachel Palmieri; Pearce, Celeste Leigh; Pejovic, Tanja; Pelttari, Liisa M; Permuth-Wey, Jennifer; Phelan, Catherine M; Pike, Malcolm C; Poole, Elizabeth M; Ramus, Susan J; Risch, Harvey A; Rosen, Barry; Rossing, Mary Anne; Rothstein, Joseph H; Rudolph, Anja; Runnebaum, Ingo B; Rzepecka, Iwona K; Salvesen, Helga B; Schildkraut, Joellen M; Schwaab, Ira; Shu, Xiao-Ou; Shvetsov, Yurii B; Siddiqui, Nadeem; Sieh, Weiva; Song, Honglin; Southey, Melissa C; Sucheston-Campbell, Lara E; Tangen, Ingvild L; Teo, Soo-Hwang; Terry, Kathryn L; Thompson, Pamela J; Timorek, Agnieszka; Tsai, Ya-Yu; Tworoger, Shelley S; van Altena, Anne M; Van Nieuwenhuysen, Els; Vergote, Ignace; Vierkant, Robert A; Wang-Gohrke, Shan; Walsh, Christine; Wentzensen, Nicolas; Whittemore, Alice S; Wicklund, Kristine G; Wilkens, Lynne R; Woo, Yin-Ling; Wu, Xifeng; Wu, Anna; Yang, Hannah; Zheng, Wei; Ziogas, Argyrios; Sellers, Thomas A; Monteiro, Alvaro N A; Freedman, Matthew L; Gayther, Simon A; Pharoah, Paul D P

2015-10-01

Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations. We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. ©2015 American Association for Cancer Research.

Network-based integration of GWAS and gene expression identifies a HOX-centric network associated with serous ovarian cancer risk

PubMed Central

Kar, Siddhartha P.; Tyrer, Jonathan P.; Li, Qiyuan; Lawrenson, Kate; Aben, Katja K.H.; Anton-Culver, Hoda; Antonenkova, Natalia; Chenevix-Trench, Georgia; Baker, Helen; Bandera, Elisa V.; Bean, Yukie T.; Beckmann, Matthias W.; Berchuck, Andrew; Bisogna, Maria; Bjørge, Line; Bogdanova, Natalia; Brinton, Louise; Brooks-Wilson, Angela; Butzow, Ralf; Campbell, Ian; Carty, Karen; Chang-Claude, Jenny; Chen, Yian Ann; Chen, Zhihua; Cook, Linda S.; Cramer, Daniel; Cunningham, Julie M.; Cybulski, Cezary; Dansonka-Mieszkowska, Agnieszka; Dennis, Joe; Dicks, Ed; Doherty, Jennifer A.; Dörk, Thilo; du Bois, Andreas; Dürst, Matthias; Eccles, Diana; Easton, Douglas F.; Edwards, Robert P.; Ekici, Arif B.; Fasching, Peter A.; Fridley, Brooke L.; Gao, Yu-Tang; Gentry-Maharaj, Aleksandra; Giles, Graham G.; Glasspool, Rosalind; Goode, Ellen L.; Goodman, Marc T.; Grownwald, Jacek; Harrington, Patricia; Harter, Philipp; Hein, Alexander; Heitz, Florian; Hildebrandt, Michelle A.T.; Hillemanns, Peter; Hogdall, Estrid; Hogdall, Claus K.; Hosono, Satoyo; Iversen, Edwin S.; Jakubowska, Anna; Paul, James; Jensen, Allan; Ji, Bu-Tian; Karlan, Beth Y; Kjaer, Susanne K.; Kelemen, Linda E.; Kellar, Melissa; Kelley, Joseph; Kiemeney, Lambertus A.; Krakstad, Camilla; Kupryjanczyk, Jolanta; Lambrechts, Diether; Lambrechts, Sandrina; Le, Nhu D.; Lee, Alice W.; Lele, Shashi; Leminen, Arto; Lester, Jenny; Levine, Douglas A.; Liang, Dong; Lissowska, Jolanta; Lu, Karen; Lubinski, Jan; Lundvall, Lene; Massuger, Leon; Matsuo, Keitaro; McGuire, Valerie; McLaughlin, John R.; McNeish, Iain A.; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B.; Narod, Steven A.; Nedergaard, Lotte; Ness, Roberta B.; Nevanlinna, Heli; Odunsi, Kunle; Olson, Sara H.; Orlow, Irene; Orsulic, Sandra; Weber, Rachel Palmieri; Pearce, Celeste Leigh; Pejovic, Tanja; Pelttari, Liisa M.; Permuth-Wey, Jennifer; Phelan, Catherine M.; Pike, Malcolm C.; Poole, Elizabeth M.; Ramus, Susan J.; Risch, Harvey A.; Rosen, Barry; Rossing, Mary Anne; Rothstein, Joseph H.; Rudolph, Anja; Runnebaum, Ingo B.; Rzepecka, Iwona K.; Salvesen, Helga B.; Schildkraut, Joellen M.; Schwaab, Ira; Shu, Xiao-Ou; Shvetsov, Yurii B; Siddiqui, Nadeem; Sieh, Weiva; Song, Honglin; Southey, Melissa C.; Sucheston-Campbell, Lara E.; Tangen, Ingvild L.; Teo, Soo-Hwang; Terry, Kathryn L.; Thompson, Pamela J; Timorek, Agnieszka; Tsai, Ya-Yu; Tworoger, Shelley S.; van Altena, Anne M.; Van Nieuwenhuysen, Els; Vergote, Ignace; Vierkant, Robert A.; Wang-Gohrke, Shan; Walsh, Christine; Wentzensen, Nicolas; Whittemore, Alice S.; Wicklund, Kristine G.; Wilkens, Lynne R.; Woo, Yin-Ling; Wu, Xifeng; Wu, Anna; Yang, Hannah; Zheng, Wei; Ziogas, Argyrios; Sellers, Thomas A.; Monteiro, Alvaro N. A.; Freedman, Matthew L.; Gayther, Simon A.; Pharoah, Paul D. P.

2015-01-01

Background Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by co-expression may also be enriched for additional EOC risk associations. Methods We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly co-expressed with each selected TF gene in the unified microarray data set of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this data set were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Results Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P<0.05 and FDR<0.05). These results were replicated (P<0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. Conclusion We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Impact Network analysis integrating large, context-specific data sets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. PMID:26209509

Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1.

PubMed

Ding, Dewu; Sun, Xiao

2018-01-16

Shewanella oneidensis MR-1 can transfer electrons from the intracellular environment to the extracellular space of the cells to reduce the extracellular insoluble electron acceptors (Extracellular Electron Transfer, EET). Benefiting from this EET capability, Shewanella has been widely used in different areas, such as energy production, wastewater treatment, and bioremediation. Genome-wide proteomics data was used to determine the active proteins involved in activating the EET process. We identified 1012 proteins with decreased expression and 811 proteins with increased expression when the EET process changed from inactivation to activation. We then networked these proteins to construct the active protein networks, and identified the top 20 key active proteins by network centralization analysis, including metabolism- and energy-related proteins, signal and transcriptional regulatory proteins, translation-related proteins, and the EET-related proteins. We also constructed the integrated protein interaction and transcriptional regulatory networks for the active proteins, then found three exclusive active network motifs involved in activating the EET process-Bi-feedforward Loop, Regulatory Cascade with a Feedback, and Feedback with a Protein-Protein Interaction (PPI)-and identified the active proteins involved in these motifs. Both enrichment analysis and comparative analysis to the whole-genome data implicated the multiheme c -type cytochromes and multiple signal processing proteins involved in the process. Furthermore, the interactions of these motif-guided active proteins and the involved functional modules were discussed. Collectively, by using network-based methods, this work reported a proteome-wide search for the key active proteins that potentially activate the EET process.

A social network-informed latent class analysis of patterns of substance use, sexual behavior, and mental health: Social Network Study III, Winnipeg, Manitoba, Canada.

PubMed

Hopfer, Suellen; Tan, Xianming; Wylie, John L

2014-05-01

We assessed whether a meaningful set of latent risk profiles could be identified in an inner-city population through individual and network characteristics of substance use, sexual behaviors, and mental health status. Data came from 600 participants in Social Network Study III, conducted in 2009 in Winnipeg, Manitoba, Canada. We used latent class analysis (LCA) to identify risk profiles and, with covariates, to identify predictors of class. A 4-class model of risk profiles fit the data best: (1) solitary users reported polydrug use at the individual level, but low probabilities of substance use or concurrent sexual partners with network members; (2) social-all-substance users reported polydrug use at the individual and network levels; (3) social-noninjection drug users reported less likelihood of injection drug and solvent use; (4) low-risk users reported low probabilities across substances. Unstable housing, preadolescent substance use, age, and hepatitis C status predicted risk profiles. Incorporation of social network variables into LCA can distinguish important subgroups with varying patterns of risk behaviors that can lead to sexually transmitted and bloodborne infections.

Functional Module Search in Protein Networks based on Semantic Similarity Improves the Analysis of Proteomics Data*

PubMed Central

Boyanova, Desislava; Nilla, Santosh; Klau, Gunnar W.; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus

2014-01-01

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system. PMID:24807868

Net Venn - An integrated network analysis web platform for gene lists

USDA-ARS?s Scientific Manuscript database

Many lists containing biological identifiers such as gene lists have been generated in various genomics projects. Identifying the overlap among gene lists can enable us to understand the similarities and differences between the datasets. Here, we present an interactome network-based web application...

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

ERIC Educational Resources Information Center

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

Do academic knowledge brokers exist? Using social network analysis to explore academic research-to-policy networks from six schools of public health in Kenya

PubMed Central

Jessani, Nasreen S; Boulay, Marc G; Bennett, Sara C

2016-01-01

The potential for academic research institutions to facilitate knowledge exchange and influence evidence-informed decision-making has been gaining ground. Schools of public health (SPHs) may play a key knowledge brokering role—serving as agencies of and for development. Understanding academic-policymaker networks can facilitate the enhancement of links between policymakers and academic faculty at SPHs, as well as assist in identifying academic knowledge brokers (KBs). Using a census approach, we administered a sociometric survey to academic faculty across six SPHs in Kenya to construct academic-policymaker networks. We identified academic KBs using social network analysis (SNA) in a two-step approach: First, we ranked individuals based on (1) number of policymakers in their network; (2) number of academic peers who report seeking them out for advice on knowledge translation and (3) their network position as ‘inter-group connectors’. Second, we triangulated the three scores and re-ranked individuals. Academic faculty scoring within the top decile across all three measures were classified as KBs. Results indicate that each SPH commands a variety of unique as well as overlapping relationships with national ministries in Kenya. Of 124 full-time faculty, we identified 7 KBs in 4 of the 6 SPHs. Those scoring high on the first measure were not necessarily the same individuals scoring high on the second. KBs were also situated in a wide range along the ‘connector/betweenness’ measure. We propose that a composite score rather than traditional ‘betweenness centrality’, provides an alternative means of identifying KBs within these networks. In conclusion, SNA is a valuable tool for identifying academic-policymaker networks in Kenya. More efforts to conduct similar network studies would permit SPH leadership to identify existing linkages between faculty and policymakers, shared linkages with other SPHs and gaps so as to contribute to evidence-informed health policies. PMID:26537610

Network Analysis in Community Psychology: Looking Back, Looking Forward.

PubMed

Neal, Zachary P; Neal, Jennifer Watling

2017-09-01

Network analysis holds promise for community psychology given the field's aim to understand the interplay between individuals and their social contexts. Indeed, because network analysis focuses explicitly on patterns of relationships between actors, its theories and methods are inherently extra-individual in nature and particularly well suited to characterizing social contexts. But, to what extent has community psychology taken advantage of this network analysis as a tool for capturing context? To answer these questions, this study provides a review of the use network analysis in articles published in American Journal of Community Psychology. Looking back, we describe and summarize the ways that network analysis has been employed in community psychology research to understand the range of ways community psychologists have found the technique helpful. Looking forward and paying particular attention to analytic issues identified in past applications, we provide some recommendations drawn from the network analysis literature to facilitate future applications of network analysis in community psychology. © 2017 The Authors. American Journal of Community Psychology published by Wiley Periodicals, Inc. on behalf of Society for Community Research and Action.

Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis.

PubMed

Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen

2016-08-18

The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

Automatic Network Fingerprinting through Single-Node Motifs

PubMed Central

Echtermeyer, Christoph; da Fontoura Costa, Luciano; Rodrigues, Francisco A.; Kaiser, Marcus

2011-01-01

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs—a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks. PMID:21297963

[Not Available].

PubMed

Yanashima, Ryoji; Kitagawa, Noriyuki; Matsubara, Yoshiya; Weatheritt, Robert; Oka, Kotaro; Kikuchi, Shinichi; Tomita, Masaru; Ishizaki, Shun

2009-01-01

The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path.

Using Social Networking to Understand Social Networks: Analysis of a Mobile Phone Closed User Group Used by a Ghanaian Health Team

PubMed Central

Akosah, Eric; Ohemeng-Dapaah, Seth; Sakyi Baah, Joseph; Kanter, Andrew S

2013-01-01

Background The network structure of an organization influences how well or poorly an organization communicates and manages its resources. In the Millennium Villages Project site in Bonsaaso, Ghana, a mobile phone closed user group has been introduced for use by the Bonsaaso Millennium Villages Project Health Team and other key individuals. No assessment on the benefits or barriers of the use of the closed user group had been carried out. Objective The purpose of this research was to make the case for the use of social network analysis methods to be applied in health systems research—specifically related to mobile health. Methods This study used mobile phone voice records of, conducted interviews with, and reviewed call journals kept by a mobile phone closed user group consisting of the Bonsaaso Millennium Villages Project Health Team. Social network analysis methodology complemented by a qualitative component was used. Monthly voice data of the closed user group from Airtel Bharti Ghana were analyzed using UCINET and visual depictions of the network were created using NetDraw. Interviews and call journals kept by informants were analyzed using NVivo. Results The methodology was successful in helping identify effective organizational structure. Members of the Health Management Team were the more central players in the network, rather than the Community Health Nurses (who might have been expected to be central). Conclusions Social network analysis methodology can be used to determine the most productive structure for an organization or team, identify gaps in communication, identify key actors with greatest influence, and more. In conclusion, this methodology can be a useful analytical tool, especially in the context of mobile health, health services, and operational and managerial research. PMID:23552721

Using social networking to understand social networks: analysis of a mobile phone closed user group used by a Ghanaian health team.

PubMed

Kaonga, Nadi Nina; Labrique, Alain; Mechael, Patricia; Akosah, Eric; Ohemeng-Dapaah, Seth; Sakyi Baah, Joseph; Kodie, Richmond; Kanter, Andrew S; Levine, Orin

2013-04-03

The network structure of an organization influences how well or poorly an organization communicates and manages its resources. In the Millennium Villages Project site in Bonsaaso, Ghana, a mobile phone closed user group has been introduced for use by the Bonsaaso Millennium Villages Project Health Team and other key individuals. No assessment on the benefits or barriers of the use of the closed user group had been carried out. The purpose of this research was to make the case for the use of social network analysis methods to be applied in health systems research--specifically related to mobile health. This study used mobile phone voice records of, conducted interviews with, and reviewed call journals kept by a mobile phone closed user group consisting of the Bonsaaso Millennium Villages Project Health Team. Social network analysis methodology complemented by a qualitative component was used. Monthly voice data of the closed user group from Airtel Bharti Ghana were analyzed using UCINET and visual depictions of the network were created using NetDraw. Interviews and call journals kept by informants were analyzed using NVivo. The methodology was successful in helping identify effective organizational structure. Members of the Health Management Team were the more central players in the network, rather than the Community Health Nurses (who might have been expected to be central). Social network analysis methodology can be used to determine the most productive structure for an organization or team, identify gaps in communication, identify key actors with greatest influence, and more. In conclusion, this methodology can be a useful analytical tool, especially in the context of mobile health, health services, and operational and managerial research.

The Strategic Environment Assessment bibliographic network: A quantitative literature review analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caschili, Simone, E-mail: s.caschili@ucl.ac.uk; De Montis, Andrea; Ganciu, Amedeo

2014-07-01

Academic literature has been continuously growing at such a pace that it can be difficult to follow the progression of scientific achievements; hence, the need to dispose of quantitative knowledge support systems to analyze the literature of a subject. In this article we utilize network analysis tools to build a literature review of scientific documents published in the multidisciplinary field of Strategic Environment Assessment (SEA). The proposed approach helps researchers to build unbiased and comprehensive literature reviews. We collect information on 7662 SEA publications and build the SEA Bibliographic Network (SEABN) employing the basic idea that two publications are interconnectedmore » if one cites the other. We apply network analysis at macroscopic (network architecture), mesoscopic (sub graph) and microscopic levels (node) in order to i) verify what network structure characterizes the SEA literature, ii) identify the authors, disciplines and journals that are contributing to the international discussion on SEA, and iii) scrutinize the most cited and important publications in the field. Results show that the SEA is a multidisciplinary subject; the SEABN belongs to the class of real small world networks with a dominance of publications in Environmental studies over a total of 12 scientific sectors. Christopher Wood, Olivia Bina, Matthew Cashmore, and Andrew Jordan are found to be the leading authors while Environmental Impact Assessment Review is by far the scientific journal with the highest number of publications in SEA studies. - Highlights: • We utilize network analysis to analyze scientific documents in the SEA field. • We build the SEA Bibliographic Network (SEABN) of 7662 publications. • We apply network analysis at macroscopic, mesoscopic and microscopic network levels. • We identify SEABN architecture, relevant publications, authors, subjects and journals.« less

The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure

PubMed Central

Goekoop, Rutger; Goekoop, Jaap G.; Scholte, H. Steven

2012-01-01

Introduction Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. Aim To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). Methods 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. Results At facet level, NCS showed a best match (96.2%) with a ‘confirmatory’ 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with ‘confirmatory’ 5-FS and ‘exploratory’ 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. Conclusion We present the first optimized network graph of personality traits according to the NEO-PI-R: a ‘Personality Web’. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network. PMID:23284713

The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

PubMed

Goekoop, Rutger; Goekoop, Jaap G; Scholte, H Steven

2012-01-01

Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. At facet level, NCS showed a best match (96.2%) with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

PubMed Central

Mason, Mike J; Fan, Guoping; Plath, Kathrin; Zhou, Qing; Horvath, Steve

2009-01-01

Background Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes. Results We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation. Conclusion Our systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology. PMID:19619308

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon.

PubMed

Koda, Satoru; Onda, Yoshihiko; Matsui, Hidetoshi; Takahagi, Kotaro; Yamaguchi-Uehara, Yukiko; Shimizu, Minami; Inoue, Komaki; Yoshida, Takuhiro; Sakurai, Tetsuya; Honda, Hiroshi; Eguchi, Shinto; Nishii, Ryuei; Mochida, Keiichi

2017-01-01

We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon . To reveal the diurnal changes in the transcriptome in B. distachyon , we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon . On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon , aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

Social network types and functional dependency in older adults in Mexico.

PubMed

Doubova Dubova, Svetlana Vladislavovna; Pérez-Cuevas, Ricardo; Espinosa-Alarcón, Patricia; Flores-Hernández, Sergio

2010-02-27

Social networks play a key role in caring for older adults. A better understanding of the characteristics of different social networks types (TSNs) in a given community provides useful information for designing policies to care for this age group. Therefore this study has three objectives: 1) To derive the TSNs among older adults affiliated with the Mexican Institute of Social Security; 2) To describe the main characteristics of the older adults in each TSN, including the instrumental and economic support they receive and their satisfaction with the network; 3) To determine the association between functional dependency and the type of social network. Secondary data analysis of the 2006 Survey of Autonomy and Dependency (N = 3,348). The TSNs were identified using the structural approach and cluster analysis. The association between functional dependency and the TSNs was evaluated with Poisson regression with robust variance analysis in which socio-demographic characteristics, lifestyle and medical history covariates were included. We identified five TSNs: diverse with community participation (12.1%), diverse without community participation (44.3%); widowed (32.0%); nonfriends-restricted (7.6%); nonfamily-restricted (4.0%). Older adults belonging to widowed and restricted networks showed a higher proportion of dependency, negative self-rated health and depression. Older adults with functional dependency more likely belonged to a widowed network (adjusted prevalence ratio 1.5; 95%CI: 1.1-2.1). The derived TSNs were similar to those described in developed countries. However, we identified the existence of a diverse network without community participation and a widowed network that have not been previously described. These TSNs and restricted networks represent a potential unmet need of social security affiliates.

Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses

PubMed Central

Luo, Jie; Xu, Pei; Cao, Peijian; Wan, Hongjian; Lv, Xiaonan; Xu, Shengchun; Wang, Gangjun; Cook, Melloni N.; Jones, Byron C.; Lu, Lu; Wang, Xusheng

2018-01-01

Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE) but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1), down-regulation in NOE but rescue in RSE (pattern 2), up-regulation in both restraint stress followed by a saline injection (RSS) and NOE, and further amplification in RSE (pattern 3), and up-regulation in RSS but reduction in both NOE and RSE (pattern 4). We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs) to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA) signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses. PMID:29674951

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network.

PubMed

Liu, Zhen; Song, Yan

2017-06-01

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network. From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway. These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.

Network propagation in the cytoscape cyberinfrastructure.

PubMed

Carlin, Daniel E; Demchak, Barry; Pratt, Dexter; Sage, Eric; Ideker, Trey

2017-10-01

Network propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib.

Social Network Analysis as a Methodological Approach to Explore Health Systems: A Case Study Exploring Support among Senior Managers/Executives in a Hospital Network.

PubMed

De Brún, Aoife; McAuliffe, Eilish

2018-03-13

Health systems research recognizes the complexity of healthcare, and the interacting and interdependent nature of components of a health system. To better understand such systems, innovative methods are required to depict and analyze their structures. This paper describes social network analysis as a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations. Through use of a case study exploring support relationships among senior managers in a newly established hospital group, this paper illustrates some of the commonly used network- and node-level metrics in social network analysis, and demonstrates the value of these maps and metrics to understand systems. Network analysis offers a valuable approach to health systems and services researchers as it offers a means to depict activity relevant to network questions of interest, to identify opinion leaders, influencers, clusters in the network, and those individuals serving as bridgers across clusters. The strengths and limitations inherent in the method are discussed, and the applications of social network analysis in health services research are explored.

High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Transport Protocol (Transmission Control Protocol/User Datagram Protocol [TCP/UDP]) Analysis

DTIC Science & Technology

2015-09-01

the network Mac8 Medium Access Control ( Mac ) (Ethernet) address observed as destination for outgoing packets subsessionid8 Zero-based index of...15. SUBJECT TERMS tactical networks, data reduction, high-performance computing, data analysis, big data 16. SECURITY CLASSIFICATION OF: 17...Integer index of row cts_deid Device (instrument) Identifier where observation took place cts_collpt Collection point or logical observation point on

Functional brain networks associated with eating behaviors in obesity.

PubMed

Park, Bo-Yong; Seo, Jongbum; Park, Hyunjin

2016-03-31

Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores.

Identifying critical transitions and their leading biomolecular networks in complex diseases.

PubMed

Liu, Rui; Li, Meiyi; Liu, Zhi-Ping; Wu, Jiarui; Chen, Luonan; Aihara, Kazuyuki

2012-01-01

Identifying a critical transition and its leading biomolecular network during the initiation and progression of a complex disease is a challenging task, but holds the key to early diagnosis and further elucidation of the essential mechanisms of disease deterioration at the network level. In this study, we developed a novel computational method for identifying early-warning signals of the critical transition and its leading network during a disease progression, based on high-throughput data using a small number of samples. The leading network makes the first move from the normal state toward the disease state during a transition, and thus is causally related with disease-driving genes or networks. Specifically, we first define a state-transition-based local network entropy (SNE), and prove that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. The effectiveness of this method was validated by functional analysis and experimental data.

Decreased triple network connectivity in patients with post-traumatic stress disorder

NASA Astrophysics Data System (ADS)

Liu, Yang; Li, Liang; Li, Baojuan; Zhang, Xi; Lu, Hongbing

2017-03-01

The triple network model provides a common framework for understanding affective and neurocognitive dysfunctions across multiple disorders, including central executive network (CEN), default mode network (DMN), and salience network (SN). Considering the effect of traumatic experience on post-traumatic stress disorder (PTSD), this study aims to explore the alteration of triple network connectivity in a specific PTSD induced by a single prolonged trauma exposure. With arterial spin labeling sequence, three networks were identified using independent component analysis in 10 PTSD patients and 10 healthy survivors, who experienced the same coal mining flood disaster. In PTSD patients, decreased connectivity was identified in left middle frontal gyrus of CEN, left precuneus and bilateral superior frontal gyrus of DMN, and right anterior insula of SN. The decreased connectivity in left middle frontal gyrus was identified to associate with clinical severity. These results indicated the decreased triple network connectivity, which not only supported the proposal of the triple network model, but also prompted possible neurobiology mechanism of cognitive dysfunction for this kind of PTSD.

Applying social network analysis to understand the knowledge sharing behaviour of practitioners in a clinical online discussion forum.

PubMed

Stewart, Samuel Alan; Abidi, Syed Sibte Raza

2012-12-04

Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment--an online discussion forum--for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is strong interprofessional and interregional communication, but a dearth of non-nurse participants has been identified as a shortcoming. The results of the analysis suggest that the discussion forum is active and healthy, and that, though few, the interprofessional and interinstitutional ties are strong.

Evidence for Functional Networks within the Human Brain's White Matter.

PubMed

Peer, Michael; Nitzan, Mor; Bick, Atira S; Levin, Netta; Arzy, Shahar

2017-07-05

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry. SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders. Copyright © 2017 the authors 0270-6474/17/376394-14$15.00/0.

Characterization of biomarkers in stroke based on ego-networks and pathways.

PubMed

Li, Haixia; Guo, Qianqian

2017-12-01

To explore potential biomarkers in stroke based on ego-networks and pathways. EgoNet method was applied to search for the underlying biomarkers in stroke using transcription profiling of E-GEOD-58294 and protein-protein interaction (PPI) data. Eight ego-genes were identified from PPI network according to the degree characteristics at the criteria of top 5% ranked z-sore and degree >1. Eight candidate ego-networks with classification accuracy ≥0.9 were selected. After performed randomization test, seven significant ego-networks with adjusted p value < 0.05 were identified. Pathway enrichment analysis was then conducted with these ego-networks to search for the significant pathways. Finally, two significant pathways were identified, and six of seven ego-networks were enriched to "3'-UTR-mediated translational regulation" pathway, indicating that this pathway performs an important role in the development of stroke. Seven ego-networks were constructed using EgoNet and two significant enriched by pathways were identified. These may provide new insights into the potential biomarkers for the development of stroke.

Review of Literature on Mentorship Networks in Medicine: Where Are We Now and Where Are We Going?

NASA Astrophysics Data System (ADS)

Mickelson, Jennifer Judith

Mentorship is imperative in medical training and conceptual frameworks for mentoring continue to evolve. This study is an integrated review of the literature on mentoring networks. A systematic review of the literature on mentoring networks identified 943 articles from multiple databases. 24 relevant articles under went qualitative analysis. An iterative approach was taken to formulate themes, subthemes and codes. Three major themes were identified. The first theme was that group or peer networks meet evolving and dynamic or changing needs through training and career development. A prominent subtheme was identified which was the need for mentees to be the architects or directors of their evolving mentorship networks. The second theme identified was that mentorship networks offered a solution to barriers associated with the dyad model of mentorship. The third theme was the importance of the informality or "voluntary marriages", as distinguished from structured formal programs, to create meaningful mentorship networks. Future directions of study include examining how to empower mentees to facilitate and direct their mentorship networks.

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

PubMed

Ji, Jiadong; He, Di; Feng, Yang; He, Yong; Xue, Fuzhong; Xie, Lei

2017-10-01

A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. R scripts available at https://github.com/jijiadong/JDINAC. lxie@iscb.org. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Information seeking for making evidence-informed decisions: a social network analysis on the staff of a public health department in Canada.

PubMed

Yousefi-Nooraie, Reza; Dobbins, Maureen; Brouwers, Melissa; Wakefield, Patricia

2012-05-16

Social network analysis is an approach to study the interactions and exchange of resources among people. It can help understanding the underlying structural and behavioral complexities that influence the process of capacity building towards evidence-informed decision making. A social network analysis was conducted to understand if and how the staff of a public health department in Ontario turn to peers to get help incorporating research evidence into practice. The staff were invited to respond to an online questionnaire inquiring about information seeking behavior, identification of colleague expertise, and friendship status. Three networks were developed based on the 170 participants. Overall shape, key indices, the most central people and brokers, and their characteristics were identified. The network analysis showed a low density and localized information-seeking network. Inter-personal connections were mainly clustered by organizational divisions; and people tended to limit information-seeking connections to a handful of peers in their division. However, recognition of expertise and friendship networks showed more cross-divisional connections. Members of the office of the Medical Officer of Health were located at the heart of the department, bridging across divisions. A small group of professional consultants and middle managers were the most-central staff in the network, also connecting their divisions to the center of the information-seeking network. In each division, there were some locally central staff, mainly practitioners, who connected their neighboring peers; but they were not necessarily connected to other experts or managers. The methods of social network analysis were useful in providing a systems approach to understand how knowledge might flow in an organization. The findings of this study can be used to identify early adopters of knowledge translation interventions, forming Communities of Practice, and potential internal knowledge brokers.

Introduction to Social Network Analysis

NASA Astrophysics Data System (ADS)

Zaphiris, Panayiotis; Ang, Chee Siang

Social Network analysis focuses on patterns of relations between and among people, organizations, states, etc. It aims to describe networks of relations as fully as possible, identify prominent patterns in such networks, trace the flow of information through them, and discover what effects these relations and networks have on people and organizations. Social network analysis offers a very promising potential for analyzing human-human interactions in online communities (discussion boards, newsgroups, virtual organizations). This Tutorial provides an overview of this analytic technique and demonstrates how it can be used in Human Computer Interaction (HCI) research and practice, focusing especially on Computer Mediated Communication (CMC). This topic acquires particular importance these days, with the increasing popularity of social networking websites (e.g., youtube, myspace, MMORPGs etc.) and the research interest in studying them.

DOE Office of Scientific and Technical Information (OSTI.GOV)

The Analysis of Search Results for the Clarification and Identification of Technology Emergence (AR-CITE) computer code examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e. scholarly publications and citation, world patents, news archives, and on-line mapping networks) are assembled to become one collective networkmore » (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the subject domain to be clarified and identified.« less

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study

PubMed Central

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias

2018-01-01

Abstract Objective To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) (“living” network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Design Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Data sources Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Eligibility criteria for study selection Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10). Outcomes and analysis Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. Results 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. Conclusions In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. PMID:29490922

Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations

PubMed Central

Beleva Guthrie, Violeta; Masica, David L; Fraser, Andrew; Federico, Joseph; Fan, Yunfan; Camps, Manel; Karchin, Rachel

2018-01-01

Abstract The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous β-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure. PMID:29522102

Transitions in Smokers’ Social Networks After Quit Attempts: A Latent Transition Analysis

PubMed Central

Smith, Rachel A.; Piper, Megan E.; Roberts, Linda J.; Baker, Timothy B.

2016-01-01

Introduction: Smokers’ social networks vary in size, composition, and amount of exposure to smoking. The extent to which smokers’ social networks change after a quit attempt is unknown, as is the relation between quitting success and later network changes. Methods: Unique types of social networks for 691 smokers enrolled in a smoking-cessation trial were identified based on network size, new network members, members’ smoking habits, within network smoking, smoking buddies, and romantic partners’ smoking. Latent transition analysis was used to identify the network classes and to predict transitions in class membership across 3 years from biochemically assessed smoking abstinence. Results: Five network classes were identified: Immersed (large network, extensive smoking exposure including smoking buddies), Low Smoking Exposure (large network, minimal smoking exposure), Smoking Partner (small network, smoking exposure primarily from partner), Isolated (small network, minimal smoking exposure), and Distant Smoking Exposure (small network, considerable nonpartner smoking exposure). Abstinence at years 1 and 2 was associated with shifts in participants’ social networks to less contact with smokers and larger networks in years 2 and 3. Conclusions: In the years following a smoking-cessation attempt, smokers’ social networks changed, and abstinence status predicted these changes. Networks defined by high levels of exposure to smokers were especially associated with continued smoking. Abstinence, however, predicted transitions to larger social networks comprising less smoking exposure. These results support treatments that aim to reduce exposure to smoking cues and smokers, including partners who smoke. Implications: Prior research has shown that social network features predict the likelihood of subsequent smoking cessation. The current research illustrates how successful quitting predicts social network change over 3 years following a quit attempt. Specifically, abstinence predicts transitions to networks that are larger and afford less exposure to smokers. This suggests that quitting smoking may expand a person’s social milieu rather than narrow it. This effect, plus reduced exposure to smokers, may help sustain abstinence. PMID:27613925

Sex Venue-Based Network Analysis to Identify HIV Prevention Dissemination Targets for Men Who Have Sex with Men.

PubMed

Patel, Rupa R; Luke, Douglas A; Proctor, Enola K; Powderly, William G; Chan, Philip A; Mayer, Kenneth H; Harrison, Laura C; Dhand, Amar

2018-01-01

The aim of this study was to identify sex venue-based networks among men who have sex with men (MSM) to inform HIV preexposure prophylaxis (PrEP) dissemination efforts. Using a cross-sectional design, we interviewed MSM about the venues where their recent sexual partners were found. Venues were organized into network matrices grouped by condom use and race. We examined network structure, central venues, and network subgroups. Among 49 participants, the median age was 27 years, 49% were Black and 86% reported condomless anal sex (ncAS). Analysis revealed a map of 54 virtual and physical venues with an overlap in the ncAS and with condom anal sex (cAS) venues. In the ncAS network, virtual and physical locations were more interconnected. The ncAS venues reported by Blacks were more diffusely organized than those reported by Whites. The network structures of sex venues for at-risk MSM differed by race. Network information can enhance HIV prevention dissemination efforts among subpopulations, including PrEP implementation.

Sex Venue-Based Network Analysis to Identify HIV Prevention Dissemination Targets for Men Who Have Sex with Men

PubMed Central

Luke, Douglas A.; Proctor, Enola K.; Powderly, William G.; Chan, Philip A.; Mayer, Kenneth H.; Harrison, Laura C.; Dhand, Amar

2018-01-01

Abstract Purpose: The aim of this study was to identify sex venue-based networks among men who have sex with men (MSM) to inform HIV preexposure prophylaxis (PrEP) dissemination efforts. Methods: Using a cross-sectional design, we interviewed MSM about the venues where their recent sexual partners were found. Venues were organized into network matrices grouped by condom use and race. We examined network structure, central venues, and network subgroups. Results: Among 49 participants, the median age was 27 years, 49% were Black and 86% reported condomless anal sex (ncAS). Analysis revealed a map of 54 virtual and physical venues with an overlap in the ncAS and with condom anal sex (cAS) venues. In the ncAS network, virtual and physical locations were more interconnected. The ncAS venues reported by Blacks were more diffusely organized than those reported by Whites. Conclusion: The network structures of sex venues for at-risk MSM differed by race. Network information can enhance HIV prevention dissemination efforts among subpopulations, including PrEP implementation. PMID:29324178

Friendship Group Composition and Juvenile Institutional Misconduct.

PubMed

Reid, Shannon E

2017-02-01

The present study examines both the patterns of friendship networks and how these network characteristics relate to the risk factors of institutional misconduct for incarcerated youth. Using friendship networks collected from males incarcerated with California's Division of Juvenile Justice (DJJ), latent profile analysis was utilized to create homogeneous groups of friendship patterns based on alter attributes and network structure. The incarcerated youth provided 144 egocentric networks reporting 558 social network relationships. Latent profile analysis identified three network profiles: expected group (67%), new breed group (20%), and model citizen group (13%). The three network profiles were integrated into a multiple group analysis framework to examine the relative influence of individual-level risk factors on their rate of institutional misconduct. The analysis finds variation in predictors of institutional misconduct across profile types. These findings suggest that the close friendships of incarcerated youth are patterned across the individual characteristics of the youth's friends and that the friendship network can act as a moderator for individual risk factors for institutional misconduct.

Evolution of the Max and Mlx networks in animals.

PubMed

McFerrin, Lisa G; Atchley, William R

2011-01-01

Transcription factors (TFs) are essential for the regulation of gene expression and often form emergent complexes to perform vital roles in cellular processes. In this paper, we focus on the parallel Max and Mlx networks of TFs because of their critical involvement in cell cycle regulation, proliferation, growth, metabolism, and apoptosis. A basic-helix-loop-helix-zipper (bHLHZ) domain mediates the competitive protein dimerization and DNA binding among Max and Mlx network members to form a complex system of cell regulation. To understand the importance of these network interactions, we identified the bHLHZ domain of Max and Mlx network proteins across the animal kingdom and carried out several multivariate statistical analyses. The presence and conservation of Max and Mlx network proteins in animal lineages stemming from the divergence of Metazoa indicate that these networks have ancient and essential functions. Phylogenetic analysis of the bHLHZ domain identified clear relationships among protein families with distinct points of radiation and divergence. Multivariate discriminant analysis further isolated specific amino acid changes within the bHLHZ domain that classify proteins, families, and network configurations. These analyses on Max and Mlx network members provide a model for characterizing the evolution of TFs involved in essential networks.

Algorithm to Identify Frequent Coupled Modules from Two-Layered Network Series: Application to Study Transcription and Splicing Coupling

PubMed Central

Li, Wenyuan; Dai, Chao; Liu, Chun-Chi

2012-01-01

Abstract Current network analysis methods all focus on one or multiple networks of the same type. However, cells are organized by multi-layer networks (e.g., transcriptional regulatory networks, splicing regulatory networks, protein-protein interaction networks), which interact and influence each other. Elucidating the coupling mechanisms among those different types of networks is essential in understanding the functions and mechanisms of cellular activities. In this article, we developed the first computational method for pattern mining across many two-layered graphs, with the two layers representing different types yet coupled biological networks. We formulated the problem of identifying frequent coupled clusters between the two layers of networks into a tensor-based computation problem, and proposed an efficient solution to solve the problem. We applied the method to 38 two-layered co-transcription and co-splicing networks, derived from 38 RNA-seq datasets. With the identified atlas of coupled transcription-splicing modules, we explored to what extent, for which cellular functions, and by what mechanisms transcription-splicing coupling takes place. PMID:22697243

Functional connectivity decreases in autism in emotion, self, and face circuits identified by Knowledge-based Enrichment Analysis.

PubMed

Cheng, Wei; Rolls, Edmund T; Zhang, Jie; Sheng, Wenbo; Ma, Liang; Wan, Lin; Luo, Qiang; Feng, Jianfeng

2017-03-01

A powerful new method is described called Knowledge based functional connectivity Enrichment Analysis (KEA) for interpreting resting state functional connectivity, using circuits that are functionally identified using search terms with the Neurosynth database. The method derives its power by focusing on neural circuits, sets of brain regions that share a common biological function, instead of trying to interpret single functional connectivity links. This provides a novel way of investigating how task- or function-related networks have resting state functional connectivity differences in different psychiatric states, provides a new way to bridge the gap between task and resting-state functional networks, and potentially helps to identify brain networks that might be treated. The method was applied to interpreting functional connectivity differences in autism. Functional connectivity decreases at the network circuit level in 394 patients with autism compared with 473 controls were found in networks involving the orbitofrontal cortex, anterior cingulate cortex, middle temporal gyrus cortex, and the precuneus, in networks that are implicated in the sense of self, face processing, and theory of mind. The decreases were correlated with symptom severity. Copyright © 2017. Published by Elsevier Inc.

In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer

PubMed Central

Colaprico, Antonio; Bontempi, Gianluca; Castiglioni, Isabella

2018-01-01

Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC. PMID:29562723

Network meta-analyses performed by contracting companies and commissioned by industry.

PubMed

Schuit, Ewoud; Ioannidis, John Pa

2016-11-25

Industry commissions contracting companies to perform network meta-analysis for health technology assessment (HTA) and reimbursement submissions. Our objective was to estimate the number of network meta-analyses performed by consulting companies contracted by industry, to assess whether they were published, and to explore reasons for non-publication. We searched MEDLINE for network meta-analyses of randomized trials. Papers were included if they had authors affiliated with any contracting company. All identified contracting companies as well as additional ones from the list of the exhibitors at the International Society for Pharmacoeconomics and Outcomes Research, an annual meeting that representatives from many contracting companies attend and exhibit at, were surveyed regarding conduct and publication of network meta-analyses. In 162 of 822 (20%) network meta-analysis papers, authors were affiliated to 66 contracting companies. Another 36 contracting companies were identified by the exhibitors list. Three companies had no contact information and six merged with others, therefore 93 companies were contacted. Thirty seven out of ninety three (40%) companies responded, and 19 indicated that they had performed a total of 476 network meta-analyses, but only 102 (21%) papers were published. Thirteen companies that disclosed to have conducted 174 network meta-analyses (45 published) provided reasons for non-publication. Of the 129 still unpublished meta-analyses, for 40 there were plans for future publication, for 37 the sponsor did not allow publication, for 16 the contracting companies did not plan to publish the meta-analysis, for another 23 plans were unclear, and the remaining 13 were used as HTA submission. The protocol of the network meta-analysis was publically available from 11/162 (6.8%) network meta-analyses published by authors affiliated with contracting companies. There is a prolific sector of professional contracting companies that perform network meta-analyses. Industry commissions many network meta-analyses, but most are not registered before or published after analyses in the scientific literature. Mechanisms to improve publication rates of network meta-analysis commissioned by industry are warranted.

Disclosing Sexual Assault Within Social Networks: A Mixed-Method Investigation.

PubMed

Dworkin, Emily R; Pittenger, Samantha L; Allen, Nicole E

2016-03-01

Most survivors of sexual assault disclose their experiences within their social networks, and these disclosure decisions can have important implications for their entry into formal systems and well-being, but no research has directly examined these networks as a strategy to understand disclosure decisions. Using a mixed-method approach that combined survey data, social network analysis, and interview data, we investigate whom, among potential informal responders in the social networks of college students who have experienced sexual assault, survivors contact regarding their assault, and how survivors narrate the role of networks in their decisions about whom to contact. Quantitative results suggest that characteristics of survivors, their social networks, and members of these networks are associated with disclosure decisions. Using data from social network analysis, we identified that survivors tended to disclose to a smaller proportion of their network when many network members had relationships with each other or when the network had more subgroups. Our qualitative analysis helps to contextualize these findings. © Society for Community Research and Action 2016.

Diverse types of genetic variation converge on functional gene networks involved in schizophrenia.

PubMed

Gilman, Sarah R; Chang, Jonathan; Xu, Bin; Bawa, Tejdeep S; Gogos, Joseph A; Karayiorgou, Maria; Vitkup, Dennis

2012-12-01

Despite the successful identification of several relevant genomic loci, the underlying molecular mechanisms of schizophrenia remain largely unclear. We developed a computational approach (NETBAG+) that allows an integrated analysis of diverse disease-related genetic data using a unified statistical framework. The application of this approach to schizophrenia-associated genetic variations, obtained using unbiased whole-genome methods, allowed us to identify several cohesive gene networks related to axon guidance, neuronal cell mobility, synaptic function and chromosomal remodeling. The genes forming the networks are highly expressed in the brain, with higher brain expression during prenatal development. The identified networks are functionally related to genes previously implicated in schizophrenia, autism and intellectual disability. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.

Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.

PubMed

Pan, Yue; Lu, Lingyun; Chen, Junquan; Zhong, Yong; Dai, Zhehao

2018-01-01

This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established. Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes. Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma.

The use of social network analysis to examine the transmission of Salmonella spp. within a vertically integrated broiler enterprise.

PubMed

Crabb, Helen Kathleen; Allen, Joanne Lee; Devlin, Joanne Maree; Firestone, Simon Matthew; Stevenson, Mark Anthony; Gilkerson, James Rudkin

2018-05-01

To better understand factors influencing infectious agent dispersal within a livestock population information is needed on the nature and frequency of contacts between farm enterprises. This study uses social network analysis to describe the contact network within a vertically integrated broiler poultry enterprise to identify the potential horizontal and vertical transmission pathways for Salmonella spp. Nodes (farms, sheds, production facilities) were identified and the daily movement of commodities (eggs, birds, feed, litter) and people between nodes were extracted from routinely kept farm records. Three time periods were examined in detail, 1- and 8- and 17-weeks of the production cycle and contact networks were described for all movements, and by commodity and production type. All nodes were linked by at least one movement during the study period but network density was low indicating that all potential pathways between nodes did not exist. Salmonella spp. transmission via vertical or horizontal pathways can only occur along directed pathways when those pathways are present. Only two locations (breeder or feed nodes) were identified where the transmission of a single Salmonella spp. clone could theoretically percolate through the network to the broiler or processing nodes. Only the feed transmission pathway directly connected all parts of the network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

NASA Astrophysics Data System (ADS)

Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

2015-11-01

Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

Protocol for a thematic synthesis to identify key themes and messages from a palliative care research network.

PubMed

Nicholson, Emma; Murphy, Tara; Larkin, Philip; Normand, Charles; Guerin, Suzanne

2016-10-21

Research networks that facilitate collaborative research are increasing both regionally and globally and such collaborations contribute greatly to knowledge transfer particularly in health research. The Palliative Care Research Network is an Irish-based network that seeks to create opportunities and engender a collaborative environment to encourage innovative research that is relevant for policy and practice. The current review outlines a methodology to identify cross-cutting messages to identify how dissemination outputs can be optimized to ensure that key messages from this research reaches all knowledge users. Preferred reporting items for systematic review and meta-analysis protocol guidelines will inform the search and analysis plan to ensure that the synthesis of the data is as rigorous as possible. An approach based on critical interpretative synthesis will be adapted to include a thematic synthesis for the identification of higher-order themes and messages from a body of dissemination products generated by the Palliative Care Research Network. The thematic synthesis outlined in the present protocol offers a novel method of synthesising data from a focused research network that employs a variety of dissemination materials as a means of identifying key themes and messages from a specific body of research. The high-level themes and messages will be identified from the thematic synthesis, widely disseminated and targeted towards a range of stakeholders and knowledge users such as carers, health and social care professionals, policy makers and researchers.

[Analysis of Spanish research collaboration in emergency medicine: 2010-2014].

PubMed

Burbano Santos, Pablo; Fernández-Guerrero, Inés María; Martín-Sánchez, Francisco Javier; Burillo, Guillermo; Miró, Òscar

2017-10-01

To describe the structure of the Spanish emergency medicine research network or networks, researchers' roles, and patterns of collaboration between hospitals. The search for publications was carried out in the SCOPUS database for the 5-year period of 2010 to 2014. We used network analysis software to map ties between researchers and hospitals that had established at least 5 and 10 relationships, respectively, during the period under study. We calculated indicators of degree of centrality for individual scientists and hospitals and tabulated data for the main authors and centers. We identified 2626 articles with 12 372 different authors at 1134 hospitals in 75 countries. The largest number of international relations were with France, the United States, and the United Kingdom. Authors had established 93 687 connections that allowed us to identify 23 collaborating groups, the largest of which was comprised of 30 individuals. We also found 12 855 connections between hospitals that identified a single subnetwork of collaboration comprised of 19 hospitals, 1 of which was in Switzerland. Measures of centrality, intermediation, and proximity led to classification of the most important members of author and hospital networks. This analysis of research networks in emergency medicine has afforded the first details describing the relationships maintained by Spanish scientists and hospitals.

A novel method to identify hub pathways of rheumatoid arthritis based on differential pathway networks.

PubMed

Wei, Shi-Tong; Sun, Yong-Hua; Zong, Shi-Hua

2017-09-01

The aim of the current study was to identify hub pathways of rheumatoid arthritis (RA) using a novel method based on differential pathway network (DPN) analysis. The present study proposed a DPN where protein‑protein interaction (PPI) network was integrated with pathway‑pathway interactions. Pathway data was obtained from background PPI network and the Reactome pathway database. Subsequently, pathway interactions were extracted from the pathway data by building randomized gene‑gene interactions and a weight value was assigned to each pathway interaction using Spearman correlation coefficient (SCC) to identify differential pathway interactions. Differential pathway interactions were visualized using Cytoscape to construct a DPN. Topological analysis was conducted to identify hub pathways that possessed the top 5% degree distribution of DPN. Modules of DPN were mined according to ClusterONE. A total of 855 pathways were selected to build pathway interactions. By filtrating pathway interactions of weight values >0.7, a DPN with 312 nodes and 791 edges was obtained. Topological degree analysis revealed 15 hub pathways, such as heparan sulfate/heparin‑glycosaminoglycan (HS‑GAG) degradation, HS‑GAG metabolism and keratan sulfate degradation for RA based on DPN. Furthermore, hub pathways were also important in modules, which validated the significance of hub pathways. In conclusion, the proposed method is a computationally efficient way to identify hub pathways of RA, which identified 15 hub pathways that may be potential biomarkers and provide insight to future investigation and treatment of RA.

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study.

PubMed

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias; Salanti, Georgia

2018-02-28

To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) ("living" network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10). Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

PubMed

Huang, Huiyuan; Wang, Junjing; Seger, Carol; Lu, Min; Deng, Feng; Wu, Xiaoyan; He, Yuan; Niu, Chen; Wang, Jun; Huang, Ruiwang

2018-01-01

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

The relation between global migration and trade networks

NASA Astrophysics Data System (ADS)

Sgrignoli, Paolo; Metulini, Rodolfo; Schiavo, Stefano; Riccaboni, Massimo

2015-01-01

In this paper we develop a methodology to analyze and compare multiple global networks, focusing our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity, where to assure comparability across networks we apply a hypergeometric filter that lets us identify those links which intensity is significantly higher than expected. Next, proposing a new way to define country neighbors based on the most intense links in the trade network, we use spatial econometrics techniques to measure the effect of migration on international trade, while controlling for network interdependences. Overall, we find that migration significantly boosts trade across countries and we are able to identify product categories for which this effect is particularly strong.

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

PubMed

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Proteomic and computational analysis of bronchoalveolar proteins during the course of the acute respiratory distress syndrome.

PubMed

Chang, Dong W; Hayashi, Shinichi; Gharib, Sina A; Vaisar, Tomas; King, S Trevor; Tsuchiya, Mitsuhiro; Ruzinski, John T; Park, David R; Matute-Bello, Gustavo; Wurfel, Mark M; Bumgarner, Roger; Heinecke, Jay W; Martin, Thomas R

2008-10-01

Acute lung injury causes complex changes in protein expression in the lungs. Whereas most prior studies focused on single proteins, newer methods allowing the simultaneous study of many proteins could lead to a better understanding of pathogenesis and new targets for treatment. The purpose of this study was to examine the changes in protein expression in the bronchoalveolar lavage fluid (BALF) of patients during the course of the acute respiratory distress syndrome (ARDS). Using two-dimensional difference gel electrophoresis (DIGE), the expression of proteins in the BALF from patients on Days 1 (n = 7), 3 (n = 8), and 7 (n = 5) of ARDS were compared with findings in normal volunteers (n = 9). The patterns of protein expression were analyzed using principal component analysis (PCA). Biological processes that were enriched in the BALF proteins of patients with ARDS were identified using Gene Ontology (GO) analysis. Protein networks that model the protein interactions in the BALF were generated using Ingenuity Pathway Analysis. An average of 991 protein spots were detected using DIGE. Of these, 80 protein spots, representing 37 unique proteins in all of the fluids, were identified using mass spectrometry. PCA confirmed important differences between the proteins in the ARDS and normal samples. GO analysis showed that these differences are due to the enrichment of proteins involved in inflammation, infection, and injury. The protein network analysis showed that the protein interactions in ARDS are complex and redundant, and revealed unexpected central components in the protein networks. Proteomics and protein network analysis reveals the complex nature of lung protein interactions in ARDS. The results provide new insights about protein networks in injured lungs, and identify novel mediators that are likely to be involved in the pathogenesis and progression of acute lung injury.

Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.

PubMed

Sehgal, Vasudha; Seviour, Elena G; Moss, Tyler J; Mills, Gordon B; Azencott, Robert; Ram, Prahlad T

2015-01-01

MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

Comparative Network-Based Recovery Analysis and Proteomic Profiling of Neurological Changes in Valproic Acid-Treated Mice

PubMed Central

2013-01-01

Despite its prominence for characterization of complex mixtures, LC–MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein–protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes. PMID:23557376

African American Extended Family and Church-Based Social Network Typologies.

PubMed

Nguyen, Ann W; Chatters, Linda M; Taylor, Robert Joseph

2016-12-01

We examined social network typologies among African American adults and their sociodemographic correlates. Network types were derived from indicators of the family and church networks. Latent class analysis was based on a nationally representative sample of African Americans from the National Survey of American Life. Results indicated four distinct network types: ambivalent, optimal, family centered, and strained. These four types were distinguished by (a) degree of social integration, (b) network composition, and (c) level of negative interactions. In a departure from previous work, a network type composed solely of nonkin was not identified, which may reflect racial differences in social network typologies. Further, the analysis indicated that network types varied by sociodemographic characteristics. Social network typologies have several promising practice implications, as they can inform the development of prevention and intervention programs.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.

PubMed

Luo, Heng; Ye, Hao; Ng, Hui; Shi, Leming; Tong, Weida; Mattes, William; Mendrick, Donna; Hong, Huixiao

2015-01-01

As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

PubMed Central

2015-01-01

Background As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Methods Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Results Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Conclusions Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs. PMID:26424483

System data communication structures for active-control transport aircraft, volume 1

NASA Technical Reports Server (NTRS)

Hopkins, A. L.; Martin, J. H.; Brock, L. D.; Jansson, D. G.; Serben, S.; Smith, T. B.; Hanley, L. D.

1981-01-01

Candidate data communication techniques are identified, including dedicated links, local buses, broadcast buses, multiplex buses, and mesh networks. The design methodology for mesh networks is then discussed, including network topology and node architecture. Several concepts of power distribution are reviewed, including current limiting and mesh networks for power. The technology issues of packaging, transmission media, and lightning are addressed, and, finally, the analysis tools developed to aid in the communication design process are described. There are special tools to analyze the reliability and connectivity of networks and more general reliability analysis tools for all types of systems.

Leveraging Social Networks To Enhance Innovation

DTIC Science & Technology

This thesis explores the Department of the Navy’s innovation initiatives to determine how to leverage social networks to enhance innovation inside...the Navy. Using the results of a social network analysis that mapped and measured the informal Navy Innovation Network, and examining how other military...branches and industry pursue innovation , this qualitative research seeks to identify gaps and redundancies in the current Navy Innovation Network

Random Visitor: Defense against Identity Attacks in P2P Networks

NASA Astrophysics Data System (ADS)

Gu, Jabeom; Nah, Jaehoon; Kwon, Hyeokchan; Jang, Jonsoo; Park, Sehyun

Various advantages of cooperative peer-to-peer networks are strongly counterbalanced by the open nature of a distributed, serverless network. In such networks, it is relatively easy for an attacker to launch various attacks such as misrouting, corrupting, or dropping messages as a result of a successful identifier forgery. The impact of an identifier forgery is particularly severe because the whole network can be compromised by attacks such as Sybil or Eclipse. In this paper, we present an identifier authentication mechanism called random visitor, which uses one or more randomly selected peers as delegates of identity proof. Our scheme uses identity-based cryptography and identity ownership proof mechanisms collectively to create multiple, cryptographically protected indirect bindings between two peers, instantly when needed, through the delegates. Because of these bindings, an attacker cannot achieve an identifier forgery related attack against interacting peers without breaking the bindings. Therefore, our mechanism limits the possibility of identifier forgery attacks efficiently by disabling an attacker's ability to break the binding. The design rationale and framework details are presented. A security analysis shows that our scheme is strong enough against identifier related attacks and that the strength increases if there are many peers (more than several thousand) in the network.

Identifying emerging research collaborations and networks: method development.

PubMed

Dozier, Ann M; Martina, Camille A; O'Dell, Nicole L; Fogg, Thomas T; Lurie, Stephen J; Rubinstein, Eric P; Pearson, Thomas A

2014-03-01

Clinical and translational research is a multidisciplinary, collaborative team process. To evaluate this process, we developed a method to document emerging research networks and collaborations in our medical center to describe their productivity and viability over time. Using an e-mail survey, sent to 1,620 clinical and basic science full- and part-time faculty members, respondents identified their research collaborators. Initial analyses, using Pajek software, assessed the feasibility of using social network analysis (SNA) methods with these data. Nearly 400 respondents identified 1,594 collaborators across 28 medical center departments resulting in 309 networks with 5 or more collaborators. This low-burden approach yielded a rich data set useful for evaluation using SNA to: (a) assess networks at several levels of the organization, including intrapersonal (individuals), interpersonal (social), organizational/institutional leadership (tenure and promotion), and physical/environmental (spatial proximity) and (b) link with other data to assess the evolution of these networks.

Personal Network Recovery Enablers and Relapse Risks for Women With Substance Dependence

PubMed Central

Brown, Suzanne; Tracy, Elizabeth M.; Jun, MinKyoung; Park, Hyunyong; Min, Meeyoung O.

2015-01-01

We examined the experiences of women in treatment for substance dependence and their treatment providers about personal networks and recovery. We conducted six focus groups at three women’s intensive substance abuse treatment programs. Four coders used thematic analysis to guide the data coding and an iterative process to identify major themes. Coders identified social network characteristics that enabled and impeded recovery and a reciprocal relationship between internal states, relationship management, and recovery. Although women described adding individuals to their networks, they also described managing existing relationships through distancing from or isolating some members to diminish their negative impact on recovery. Treatment providers identified similar themes but focused more on contextual barriers than the women. The focus of interventions with this population should be on both internal barriers to personal network change such as mistrust and fear, and helping women develop skills for managing enduring network relationships. PMID:25231945

Scientific Collaboration in Chinese Nursing Research: A Social Network Analysis Study.

PubMed

Hou, Xiao-Ni; Hao, Yu-Fang; Cao, Jing; She, Yan-Chao; Duan, Hong-Mei

2016-01-01

Collaboration has become very important in research and in technological progress. Coauthorship networks in different fields have been intensively studied as an important type of collaboration in recent years. Yet there are few published reports about collaboration in the field of nursing. This article aimed to reveal the status and identify the key features of collaboration in the field of nursing in China. Using data from the top 10 nursing journals in China from 2003 to 2013, we constructed a nursing scientific coauthorship network using social network analysis. We found that coauthorship was a common phenomenon in the Chinese nursing field. A coauthorship network with 228 subnetworks formed by 1428 nodes was constructed. The network was relatively loose, and most subnetworks were of small scales. Scholars from Shanghai and from military medical system were at the center of the Chinese nursing scientific coauthorship network. We identified the authors' positions and influences according to the research output and centralities of each author. We also analyzed the microstructure and the evolution over time of the maximum subnetwork.

A network analysis of DSM-5 posttraumatic stress disorder and functional impairment in UK treatment-seeking veterans.

PubMed

Ross, Jana; Murphy, Dominic; Armour, Cherie

2018-05-28

Network analysis is a relatively new methodology for studying psychological disorders. It focuses on the associations between individual symptoms which are hypothesized to mutually interact with each other. The current study represents the first network analysis conducted with treatment-seeking military veterans in UK. The study aimed to examine the network structure of posttraumatic stress disorder (PTSD) symptoms and four domains of functional impairment by identifying the most central (i.e., important) symptoms of PTSD and by identifying those symptoms of PTSD that are related to functional impairment. Participants were 331 military veterans with probable PTSD. In the first step, a network of PTSD symptoms based on the PTSD Checklist for DSM-5 was estimated. In the second step, functional impairment items were added to the network. The most central symptoms of PTSD were recurrent thoughts, nightmares, negative emotional state, detachment and exaggerated startle response. Functional impairment was related to a number of different PTSD symptoms. Impairments in close relationships were associated primarily with the negative alterations in cognitions and mood symptoms and impairments in home management were associated primarily with the reexperiencing symptoms. The results are discussed in relation to previous PTSD network studies and include implications for clinical practice. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures.

PubMed

Li, Yongsheng; Sahni, Nidhi; Yi, Song

2016-11-29

Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

Quantitative Analysis of Signaling Networks across Differentially Embedded Tumors Highlights Interpatient Heterogeneity in Human Glioblastoma

PubMed Central

2015-01-01

Glioblastoma multiforme (GBM) is the most aggressive malignant primary brain tumor, with a dismal mean survival even with the current standard of care. Although in vitro cell systems can provide mechanistic insight into the regulatory networks governing GBM cell proliferation and migration, clinical samples provide a more physiologically relevant view of oncogenic signaling networks. However, clinical samples are not widely available and may be embedded for histopathologic analysis. With the goal of accurately identifying activated signaling networks in GBM tumor samples, we investigated the impact of embedding in optimal cutting temperature (OCT) compound followed by flash freezing in LN2 vs immediate flash freezing (iFF) in LN2 on protein expression and phosphorylation-mediated signaling networks. Quantitative proteomic and phosphoproteomic analysis of 8 pairs of tumor specimens revealed minimal impact of the different sample processing strategies and highlighted the large interpatient heterogeneity present in these tumors. Correlation analyses of the differentially processed tumor sections identified activated signaling networks present in selected tumors and revealed the differential expression of transcription, translation, and degradation associated proteins. This study demonstrates the capability of quantitative mass spectrometry for identification of in vivo oncogenic signaling networks from human tumor specimens that were either OCT-embedded or immediately flash-frozen. PMID:24927040

Performance Analysis of MIMO Relay Network via Propagation Measurement in L-Shaped Corridor Environment

NASA Astrophysics Data System (ADS)

Lertwiram, Namzilp; Tran, Gia Khanh; Mizutani, Keiichi; Sakaguchi, Kei; Araki, Kiyomichi

Setting relays can address the shadowing problem between a transmitter (Tx) and a receiver (Rx). Moreover, the Multiple-Input Multiple-Output (MIMO) technique has been introduced to improve wireless link capacity. The MIMO technique can be applied in relay network to enhance system performance. However, the efficiency of relaying schemes and relay placement have not been well investigated with experiment-based study. This paper provides a propagation measurement campaign of a MIMO two-hop relay network in 5GHz band in an L-shaped corridor environment with various relay locations. Furthermore, this paper proposes a Relay Placement Estimation (RPE) scheme to identify the optimum relay location, i.e. the point at which the network performance is highest. Analysis results of channel capacity show that relaying technique is beneficial over direct transmission in strong shadowing environment while it is ineffective in non-shadowing environment. In addition, the optimum relay location estimated with the RPE scheme also agrees with the location where the network achieves the highest performance as identified by network capacity. Finally, the capacity analysis shows that two-way MIMO relay employing network coding has the best performance while cooperative relaying scheme is not effective due to shadowing effect weakening the signal strength of the direct link.

Identifying and Investigating the "Best" Schools: A Network-Based Analysis

ERIC Educational Resources Information Center

Joshi, Priyadarshani

2018-01-01

This paper aims to provide a fresh perspective on the predominantly negative discourse on schooling quality in low-income countries by focusing on the research questions: "How can one identify great schools and what makes them special?" Using a network-based perspective, I measure peer evaluations of quality in two districts in Nepal.…

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

PubMed

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2018-06-07

Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs). We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems. Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.

Application of Network Analysis to Identify and Map Relationships between Information Systems in the context of Arctic Sustainability

NASA Astrophysics Data System (ADS)

Kontar, Y. Y.

2017-12-01

The Arctic Council is an intergovernmental forum promoting cooperation, coordination and interaction among the Arctic States and indigenous communities on issues of sustainable development and environmental protection in the North. The work of the Council is primarily carried out by six Working Groups: Arctic Contaminants Action Program, Arctic Monitoring and Assessment Programme, Conservation of Arctic Flora and Fauna, Emergency Prevention, Preparedness and Response, Protection of the Arctic Marine Environment, and Sustainable Development Working Group. The Working Groups are composed of researchers and representatives from government agencies. Each Working Group issues numerous scientific assessments and reports on a broad field of subjects, from climate change to emergency response in the Arctic. A key goal of these publications is to contribute to policy-making in the Arctic. Complex networks of information systems and the connections between the diverse elements within the systems have been identified via network analysis. This allowed to distinguish data sources that were used in the composition of the primary publications of the Working Groups. Next step is to implement network analysis to identify and map the relationships between the Working Groups and policy makers in the Arctic.

Transcriptional Network Analysis Identifies BACH1 as a Master Regulator of Breast Cancer Bone Metastasis

PubMed Central

Liang, Yajun; Wu, Heng; Lei, Rong; Chong, Robert A.; Wei, Yong; Lu, Xin; Tagkopoulos, Ilias; Kung, Sun-Yuan; Yang, Qifeng; Hu, Guohong; Kang, Yibin

2012-01-01

The application of functional genomic analysis of breast cancer metastasis has led to the identification of a growing number of organ-specific metastasis genes, which often function in concert to facilitate different steps of the metastatic cascade. However, the gene regulatory network that controls the expression of these metastasis genes remains largely unknown. Here, we demonstrate a computational approach for the deconvolution of transcriptional networks to discover master regulators of breast cancer bone metastasis. Several known regulators of breast cancer bone metastasis such as Smad4 and HIF1 were identified in our analysis. Experimental validation of the networks revealed BACH1, a basic leucine zipper transcription factor, as the common regulator of several functional metastasis genes, including MMP1 and CXCR4. Ectopic expression of BACH1 enhanced the malignance of breast cancer cells, and conversely, BACH1 knockdown significantly reduced bone metastasis. The expression of BACH1 and its target genes was linked to the higher risk of breast cancer recurrence in patients. This study established BACH1 as the master regulator of breast cancer bone metastasis and provided a paradigm to identify molecular determinants in complex pathological processes. PMID:22875853

Exploring Research Topics and Trends in Nursing-related Communication in Intensive Care Units Using Social Network Analysis.

PubMed

Son, Youn-Jung; Lee, Soo-Kyoung; Nam, SeJin; Shim, Jae Lan

2018-05-04

This study used social network analysis to identify the main research topics and trends in nursing-related communication in intensive care units. Keywords from January 1967 to June 2016 were extracted from PubMed using Medical Subject Headings terms. Social network analysis was performed using Gephi software. Research publications and newly emerging topics in nursing-related communication in intensive care units were classified into five chronological phases. After the weighting was adjusted, the top five keyword searches were "conflict," "length of stay," "nursing continuing education," "family," and "nurses." During the most recent phase, research topics included "critical care nursing," "patient handoff," and "quality improvement." The keywords of the top three groups among the 10 groups identified were related to "neonatal nursing and practice guideline," "infant or pediatric and terminal care," and "family, aged, and nurse-patient relations," respectively. This study can promote a systematic understanding of communication in intensive care units by identifying topic networks. Future studies are needed to conduct large prospective cohort studies and randomized controlled trials to verify the effects of patient-centered communication in intensive care units on patient outcomes, such as length of hospital stay and mortality.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

A catalyst for system change: a case study of child health network formation, evolution and sustainability in Canada.

PubMed

McPherson, Charmaine; Ploeg, Jenny; Edwards, Nancy; Ciliska, Donna; Sword, Wendy

2017-02-01

The purpose of this study was to examine key processes and supportive and inhibiting factors involved in the development, evolution, and sustainability of a child health network in rural Canada. This study contributes to a relatively new research agenda aimed at understanding inter-organizational and cross-sectoral health networks. These networks encourage collaboration focusing on complex issues impacting health - issues that individual agencies cannot effectively address alone. This paper presents an overview of the study findings. An explanatory qualitative case study approach examined the Network's 13-year lifespan. Data sources were documents and Network members, including regional and 71 provincial senior managers from 11 child and youth service sectors. Data were collected through 34 individual interviews and a review of 127 documents. Interview data were analyzed using framework analysis methods; Prior's approach guided document analysis. Three themes related to network development, evolution and sustainability were identified: (a) Network relationships as system triggers, (b) Network-mediated system responsiveness, and (c) Network practice as political. Study findings have important implications for network organizational development, collaborative practice, interprofessional education, public policy, and public system responsiveness research. Findings suggest it is important to explicitly focus on relationships and multi-level socio-political contexts, such as supportive policy environments, in understanding health networks. The dynamic interplay among the Network members; central supportive and inhibiting factors; and micro-, meso-, and macro-organizational contexts was identified.

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

PubMed Central

Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; Calhoun, Vince D

2013-01-01

Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pair-wise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. PMID:23876245

An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships

PubMed Central

Vaiman, Daniel; Miralles, Francisco

2016-01-01

Preeclampsia (PE) is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs). However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation. PMID:27802351

Getting "Plugged in": A Network Analysis of Health-Information Seeking among "Stay-At-Home Moms."

ERIC Educational Resources Information Center

Tardy, Rebecca W.; Hale, Claudia L.

1998-01-01

Examines health-oriented conversations in informal, interpersonal networks. Investigates the network structure, nature of relationships, contents of interactions, and impact of support manifested in a "stay-at-home" mothers' toddlers' play group. Identifies members' roles, and characteristics of opinion leaders, and includes anecdotal…

Discrete Mathematical Approaches to Graph-Based Traffic Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Cowley, Wendy E.; Hogan, Emilie A.

2014-04-01

Modern cyber defense and anlaytics requires general, formal models of cyber systems. Multi-scale network models are prime candidates for such formalisms, using discrete mathematical methods based in hierarchically-structured directed multigraphs which also include rich sets of labels. An exemplar of an application of such an approach is traffic analysis, that is, observing and analyzing connections between clients, servers, hosts, and actors within IP networks, over time, to identify characteristic or suspicious patterns. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. In thismore » paper, we consider traffic analysis of Netflow using both basic graph statistics and two new mathematical measures involving labeled degree distributions and time interval overlap measures. We do all of this over the VAST test data set of 96M synthetic Netflow graph edges, against which we can identify characteristic patterns of simulated ground-truth network attacks.« less

Understanding complex interactions using social network analysis.

PubMed

Pow, Janette; Gayen, Kaberi; Elliott, Lawrie; Raeside, Robert

2012-10-01

The aim of this paper is to raise the awareness of social network analysis as a method to facilitate research in nursing research. The application of social network analysis in assessing network properties has allowed greater insight to be gained in many areas including sociology, politics, business organisation and health care. However, the use of social networks in nursing has not received sufficient attention. Review of literature and illustration of the application of the method of social network analysis using research examples. First, the value of social networks will be discussed. Then by using illustrative examples, the value of social network analysis to nursing will be demonstrated. The method of social network analysis is found to give greater insights into social situations involving interactions between individuals and has particular application to the study of interactions between nurses and between nurses and patients and other actors. Social networks are systems in which people interact. Two quantitative techniques help our understanding of these networks. The first is visualisation of the network. The second is centrality. Individuals with high centrality are key communicators in a network. Applying social network analysis to nursing provides a simple method that helps gain an understanding of human interaction and how this might influence various health outcomes. It allows influential individuals (actors) to be identified. Their influence on the formation of social norms and communication can determine the extent to which new interventions or ways of thinking are accepted by a group. Thus, working with key individuals in a network could be critical to the success and sustainability of an intervention. Social network analysis can also help to assess the effectiveness of such interventions for the recipient and the service provider. © 2012 Blackwell Publishing Ltd.

Major component analysis of dynamic networks of physiologic organ interactions

NASA Astrophysics Data System (ADS)

Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch

2015-09-01

The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.

Network analysis of translocated Takahe populations to identify disease surveillance targets.

PubMed

Grange, Zoë L; VAN Andel, Mary; French, Nigel P; Gartrell, Brett D

2014-04-01

Social network analysis is being increasingly used in epidemiology and disease modeling in humans, domestic animals, and wildlife. We investigated this tool in describing a translocation network (area that allows movement of animals between geographically isolated locations) used for the conservation of an endangered flightless rail, the Takahe (Porphyrio hochstetteri). We collated records of Takahe translocations within New Zealand and used social network principles to describe the connectivity of the translocation network. That is, networks were constructed and analyzed using adjacency matrices with values based on the tie weights between nodes. Five annual network matrices were created using the Takahe data set, each incremental year included records of previous years. Weights of movements between connected locations were assigned by the number of Takahe moved. We calculated the number of nodes (i(total)) and the number of ties (t(total)) between the nodes. To quantify the small-world character of the networks, we compared the real networks to random graphs of the equivalent size, weighting, and node strength. Descriptive analysis of cumulative annual Takahe movement networks involved determination of node-level characteristics, including centrality descriptors of relevance to disease modeling such as weighted measures of in degree (k(i)(in)), out degree (k(i)(out)), and betweenness (B(i)). Key players were assigned according to the highest node measure of k(i)(in), k(i)(out), and B(i) per network. Networks increased in size throughout the time frame considered. The network had some degree small-world characteristics. Nodes with the highest cumulative tie weights connecting them were the captive breeding center, the Murchison Mountains and 2 offshore islands. The key player fluctuated between the captive breeding center and the Murchison Mountains. The cumulative networks identified the captive breeding center every year as the hub of the network until the final network in 2011. Likewise, the wild Murchison Mountains population was consistently the sink of the network. Other nodes, such as the offshore islands and the wildlife hospital, varied in importance over time. Common network descriptors and measures of centrality identified key locations for targeting disease surveillance. The visual representation of movements of animals in a population that this technique provides can aid decision makers when they evaluate translocation proposals or attempt to control a disease outbreak. © 2014 Society for Conservation Biology.

Decision-making in irrigation networks: Selecting appropriate canal structures using multi-attribute decision analysis.

PubMed

Hosseinzade, Zeinab; Pagsuyoin, Sheree A; Ponnambalam, Kumaraswamy; Monem, Mohammad J

2017-12-01

The stiff competition for water between agriculture and non-agricultural production sectors makes it necessary to have effective management of irrigation networks in farms. However, the process of selecting flow control structures in irrigation networks is highly complex and involves different levels of decision makers. In this paper, we apply multi-attribute decision making (MADM) methodology to develop a decision analysis (DA) framework for evaluating, ranking and selecting check and intake structures for irrigation canals. The DA framework consists of identifying relevant attributes for canal structures, developing a robust scoring system for alternatives, identifying a procedure for data quality control, and identifying a MADM model for the decision analysis. An application is illustrated through an analysis for automation purposes of the Qazvin irrigation network, one of the oldest and most complex irrigation networks in Iran. A survey questionnaire designed based on the decision framework was distributed to experts, managers, and operators of the Qazvin network and to experts from the Ministry of Power in Iran. Five check structures and four intake structures were evaluated. A decision matrix was generated from the average scores collected from the survey, and was subsequently solved using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. To identify the most critical structure attributes for the selection process, optimal attribute weights were calculated using Entropy method. For check structures, results show that the duckbill weir is the preferred structure while the pivot weir is the least preferred. Use of the duckbill weir can potentially address the problem with existing Amil gates where manual intervention is required to regulate water levels during periods of flow extremes. For intake structures, the Neyrpic® gate and constant head orifice are the most and least preferred alternatives, respectively. Some advantages of the Neyrpic® gate are ease of operation and capacity to measure discharge flows. Overall, the application to the Qazvin irrigation network demonstrates the utility of the proposed DA framework in selecting appropriate structures for regulating water flows in irrigation canals. This framework systematically aids the decision process by capturing decisions made at various levels (individual farmers to high-level management). It can be applied to other cases where a new irrigation network is being designed, or where changes in irrigation structures need to be identified to improve flow control in existing networks. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model

PubMed Central

Baskerville, Edward B.; Dobson, Andy P.; Bedford, Trevor; Allesina, Stefano; Anderson, T. Michael; Pascual, Mercedes

2011-01-01

Food webs, networks of feeding relationships in an ecosystem, provide fundamental insights into mechanisms that determine ecosystem stability and persistence. A standard approach in food-web analysis, and network analysis in general, has been to identify compartments, or modules, defined by many links within compartments and few links between them. This approach can identify large habitat boundaries in the network but may fail to identify other important structures. Empirical analyses of food webs have been further limited by low-resolution data for primary producers. In this paper, we present a Bayesian computational method for identifying group structure using a flexible definition that can describe both functional trophic roles and standard compartments. We apply this method to a newly compiled plant-mammal food web from the Serengeti ecosystem that includes high taxonomic resolution at the plant level, allowing a simultaneous examination of the signature of both habitat and trophic roles in network structure. We find that groups at the plant level reflect habitat structure, coupled at higher trophic levels by groups of herbivores, which are in turn coupled by carnivore groups. Thus the group structure of the Serengeti web represents a mixture of trophic guild structure and spatial pattern, in contrast to the standard compartments typically identified. The network topology supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence. Furthermore, our Bayesian approach provides a powerful, flexible framework for the study of network structure, and we believe it will prove instrumental in a variety of biological contexts. PMID:22219719

An integrated systems genetics screen reveals the transcriptional structure of inherited predisposition to metastatic disease

PubMed Central

Faraji, Farhoud; Hu, Ying; Wu, Gang; Goldberger, Natalie E.; Walker, Renard C.; Zhang, Jinghui; Hunter, Kent W.

2014-01-01

Metastasis is the result of stochastic genomic and epigenetic events leading to gene expression profiles that drive tumor dissemination. Here we exploit the principle that metastatic propensity is modified by the genetic background to generate prognostic gene expression signatures that illuminate regulators of metastasis. We also identify multiple microRNAs whose germline variation is causally linked to tumor progression and metastasis. We employ network analysis of global gene expression profiles in tumors derived from a panel of recombinant inbred mice to identify a network of co-expressed genes centered on Cnot2 that predicts metastasis-free survival. Modulating Cnot2 expression changes tumor cell metastatic potential in vivo, supporting a functional role for Cnot2 in metastasis. Small RNA sequencing of the same tumor set revealed a negative correlation between expression of the Mir216/217 cluster and tumor progression. Expression quantitative trait locus analysis (eQTL) identified cis-eQTLs at the Mir216/217 locus, indicating that differences in expression may be inherited. Ectopic expression of Mir216/217 in tumor cells suppressed metastasis in vivo. Finally, small RNA sequencing and mRNA expression profiling data were integrated to reveal that miR-3470a/b target a high proportion of network transcripts. In vivo analysis of Mir3470a/b demonstrated that both promote metastasis. Moreover, Mir3470b is a likely regulator of the Cnot2 network as its overexpression down-regulated expression of network hub genes and enhanced metastasis in vivo, phenocopying Cnot2 knockdown. The resulting data from this strategy identify Cnot2 as a novel regulator of metastasis and demonstrate the power of our systems-level approach in identifying modifiers of metastasis. PMID:24322557

Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system.

PubMed

Gao, Xiangyun; Huang, Shupei; Sun, Xiaoqi; Hao, Xiaoqing; An, Feng

2018-03-01

Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion.

Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system

PubMed Central

Huang, Shupei; Sun, Xiaoqi; Hao, Xiaoqing; An, Feng

2018-01-01

Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion. PMID:29657804

Correlated mRNAs and miRNAs from co-expression and regulatory networks affect porcine muscle and finally meat properties.

PubMed

Ponsuksili, Siriluck; Du, Yang; Hadlich, Frieder; Siengdee, Puntita; Murani, Eduard; Schwerin, Manfred; Wimmers, Klaus

2013-08-05

Physiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes. MicroRNAs regulate networks of genes to orchestrate cellular functions, in turn regulating phenotypes. We applied weighted gene co-expression network analysis to identify co-expression modules that correlated to meat quality phenotypes and were highly enriched for genes involved in glucose metabolism, response to wounding, mitochondrial ribosome, mitochondrion, and extracellular matrix. Negative correlation of miRNA with mRNA and target prediction were used to select transcripts out of the modules of trait-associated mRNAs to further identify those genes that are correlated with post mortem traits. Porcine muscle co-expression transcript networks that correlated to post mortem traits were identified. The integration of miRNA and mRNA expression analyses, as well as network analysis, enabled us to interpret the differentially-regulated genes from a systems perspective. Linking co-expression networks of transcripts and hierarchically organized pairs of miRNAs and mRNAs to meat properties yields new insight into several biological pathways underlying phenotype differences. These pathways may also be diagnostic for many myopathies, which are accompanied by deficient nutrient and oxygen supply of muscle fibers.

The Earth Science Research Network as Seen Through Network Analysis of the AGU

NASA Astrophysics Data System (ADS)

Narock, T.; Hasnain, S.; Stephan, R.

2017-12-01

Scientometrics is the science of science. Scientometric research includes measurements of impact, mapping of scientific fields, and the production of indicators for use in policy and management. We have leveraged network analysis in a scientometric study of the American Geophysical Union (AGU). Data from the AGU's Linked Data Abstract Browser was used to create a visualization and analytics tools to explore the Earth science's research network. Our application applies network theory to look at network structure within the various AGU sections, identify key individuals and communities related to Earth science topics, and examine multi-disciplinary collaboration across sections. Opportunities to optimize Earth science output, as well as policy and outreach applications, are discussed.

Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis.

PubMed

Meng, Yu-Xiu; Liu, Quan-Hong; Chen, Deng-Hong; Meng, Ying

2017-06-01

Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis. By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Connectome analysis for pre-operative brain mapping in neurosurgery

PubMed Central

Hart, Michael G.; Price, Stephen J.; Suckling, John

2016-01-01

Abstract Object: Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains ‘wiring diagram’. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences of their impairment or excision. Our goal was to apply connectome analysis in patients with brain tumours to characterise overall network topology and individual patterns of connectivity alterations. Methods: Resting-state functional MRI data were acquired using multi-echo, echo planar imaging pre-operatively from five participants each with a right temporal–parietal–occipital glioblastoma. Complex networks analysis was initiated by parcellating the brain into anatomically regions amongst which connections were identified by retaining the most significant correlations between the respective wavelet decomposed time-series. Results: Key characteristics of complex networks described in healthy controls were preserved in these patients, including ubiquitous small world organization. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness to injury but with a core of hubs exhibiting disproportionate vulnerability. Tumours produced a consistent reduction in local and long-range connectivity with distinct patterns of connection loss depending on lesion location. Conclusions: Connectome analysis is a feasible and novel approach to brain mapping in individual patients with brain tumours. Applications to pre-surgical planning include identifying regions critical to network function that should be preserved and visualising connections at risk from tumour resection. In the future one could use such data to model functional plasticity and recovery of cognitive deficits. PMID:27447756

Systematic review of social network analysis in adolescent cigarette smoking behavior.

PubMed

Seo, Dong-Chul; Huang, Yan

2012-01-01

Social networks are important in adolescent smoking behavior. Previous research indicates that peer context is a major causal factor of adolescent smoking behavior. To date, however, little is known about the influence of peer group structure on adolescent smoking behavior. Studies that examined adolescent social networks with regard to their cigarette smoking behavior were identified through online and manual literature searches. Ten social network analysis studies involving a total of 28,263 adolescents were included in the final review. Of the 10 reviewed studies, 6 identify clique members, liaisons, and isolates as contributing factors to adolescent cigarette smoking. Significantly higher rates of smoking are noted among isolates than clique members or liaisons in terms of peer network structure. Eight of the reviewed studies indicate that peer selection or influence precedes adolescents' smoking behavior and intent to smoke. Such peer selection or influence accounts for a large portion of similarities among smoking adolescents. Adolescents who are identified as isolates are more likely to smoke and engage in risk-taking behaviors than others in the peer network structure. Given that the vast majority of current adult smokers started their smoking habits during adolescence, adolescent smoking prevention efforts will likely benefit from incorporating social network analytic approaches and focusing the efforts on isolates and other vulnerable adolescents from a peer selection and influence perspective. © 2011, American School Health Association.

Comparative analysis of quantitative efficiency evaluation methods for transportation networks

PubMed Central

He, Yuxin; Hong, Jian

2017-01-01

An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess’s Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified. PMID:28399165

Comparative analysis of quantitative efficiency evaluation methods for transportation networks.

PubMed

He, Yuxin; Qin, Jin; Hong, Jian

2017-01-01

An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess's Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified.

An Examination of Not-For-Profit Stakeholder Networks for Relationship Management: A Small-Scale Analysis on Social Media.

PubMed

Wyllie, Jessica; Lucas, Benjamin; Carlson, Jamie; Kitchens, Brent; Kozary, Ben; Zaki, Mohamed

2016-01-01

Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes), connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed.

An Examination of Not-For-Profit Stakeholder Networks for Relationship Management: A Small-Scale Analysis on Social Media

PubMed Central

Carlson, Jamie; Kitchens, Brent; Kozary, Ben; Zaki, Mohamed

2016-01-01

Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes), connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed. PMID:27711236

PDB2Graph: A toolbox for identifying critical amino acids map in proteins based on graph theory.

PubMed

Niknam, Niloofar; Khakzad, Hamed; Arab, Seyed Shahriar; Naderi-Manesh, Hossein

2016-05-01

The integrative and cooperative nature of protein structure involves the assessment of topological and global features of constituent parts. Network concept takes complete advantage of both of these properties in the analysis concomitantly. High compatibility to structural concepts or physicochemical properties in addition to exploiting a remarkable simplification in the system has made network an ideal tool to explore biological systems. There are numerous examples in which different protein structural and functional characteristics have been clarified by the network approach. Here, we present an interactive and user-friendly Matlab-based toolbox, PDB2Graph, devoted to protein structure network construction, visualization, and analysis. Moreover, PDB2Graph is an appropriate tool for identifying critical nodes involved in protein structural robustness and function based on centrality indices. It maps critical amino acids in protein networks and can greatly aid structural biologists in selecting proper amino acid candidates for manipulating protein structures in a more reasonable and rational manner. To introduce the capability and efficiency of PDB2Graph in detail, the structural modification of Calmodulin through allosteric binding of Ca(2+) is considered. In addition, a mutational analysis for three well-identified model proteins including Phage T4 lysozyme, Barnase and Ribonuclease HI, was performed to inspect the influence of mutating important central residues on protein activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Proteome reference map and regulation network of neonatal rat cardiomyocyte

PubMed Central

Li, Zi-jian; Liu, Ning; Han, Qi-de; Zhang, You-yi

2011-01-01

Aim: To study and establish a proteome reference map and regulation network of neonatal rat cardiomyocyte. Methods: Cultured cardiomyocytes of neonatal rats were used. All proteins expressed in the cardiomyocytes were separated and identified by two-dimensional polyacrylamide gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). Biological networks and pathways of the neonatal rat cardiomyocytes were analyzed using the Ingenuity Pathway Analysis (IPA) program (www.ingenuity.com). A 2-DE database was made accessible on-line by Make2ddb package on a web server. Results: More than 1000 proteins were separated on 2D gels, and 148 proteins were identified. The identified proteins were used for the construction of an extensible markup language-based database. Biological networks and pathways were constructed to analyze the functions associate with cardiomyocyte proteins in the database. The 2-DE database of rat cardiomyocyte proteins can be accessed at http://2d.bjmu.edu.cn. Conclusion: A proteome reference map and regulation network of the neonatal rat cardiomyocytes have been established, which may serve as an international platform for storage, analysis and visualization of cardiomyocyte proteomic data. PMID:21841810

Transitions in Smokers' Social Networks After Quit Attempts: A Latent Transition Analysis.

PubMed

Bray, Bethany C; Smith, Rachel A; Piper, Megan E; Roberts, Linda J; Baker, Timothy B

2016-12-01

Smokers' social networks vary in size, composition, and amount of exposure to smoking. The extent to which smokers' social networks change after a quit attempt is unknown, as is the relation between quitting success and later network changes. Unique types of social networks for 691 smokers enrolled in a smoking-cessation trial were identified based on network size, new network members, members' smoking habits, within network smoking, smoking buddies, and romantic partners' smoking. Latent transition analysis was used to identify the network classes and to predict transitions in class membership across 3 years from biochemically assessed smoking abstinence. Five network classes were identified: Immersed (large network, extensive smoking exposure including smoking buddies), Low Smoking Exposure (large network, minimal smoking exposure), Smoking Partner (small network, smoking exposure primarily from partner), Isolated (small network, minimal smoking exposure), and Distant Smoking Exposure (small network, considerable nonpartner smoking exposure). Abstinence at years 1 and 2 was associated with shifts in participants' social networks to less contact with smokers and larger networks in years 2 and 3. In the years following a smoking-cessation attempt, smokers' social networks changed, and abstinence status predicted these changes. Networks defined by high levels of exposure to smokers were especially associated with continued smoking. Abstinence, however, predicted transitions to larger social networks comprising less smoking exposure. These results support treatments that aim to reduce exposure to smoking cues and smokers, including partners who smoke. Prior research has shown that social network features predict the likelihood of subsequent smoking cessation. The current research illustrates how successful quitting predicts social network change over 3 years following a quit attempt. Specifically, abstinence predicts transitions to networks that are larger and afford less exposure to smokers. This suggests that quitting smoking may expand a person's social milieu rather than narrow it. This effect, plus reduced exposure to smokers, may help sustain abstinence. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Perspectives on Social Network Analysis for Observational Scientific Data

NASA Astrophysics Data System (ADS)

Singh, Lisa; Bienenstock, Elisa Jayne; Mann, Janet

This chapter is a conceptual look at data quality issues that arise during scientific observations and their impact on social network analysis. We provide examples of the many types of incompleteness, bias and uncertainty that impact the quality of social network data. Our approach is to leverage the insights and experience of observational behavioral scientists familiar with the challenges of making inference when data are not complete, and suggest avenues for extending these to relational data questions. The focus of our discussion is on network data collection using observational methods because they contain high dimensionality, incomplete data, varying degrees of observational certainty, and potential observer bias. However, the problems and recommendations identified here exist in many other domains, including online social networks, cell phone networks, covert networks, and disease transmission networks.

Identification of PEG-induced water stress responsive transcripts using co-expression network in Eucalyptus grandis.

PubMed

Ghosh Dasgupta, Modhumita; Dharanishanthi, Veeramuthu

2017-09-05

Ecophysiological studies in Eucalyptus have shown that water is the principal factor limiting stem growth. Effect of water deficit conditions on physiological and biochemical parameters has been extensively reported in Eucalyptus. The present study was conducted to identify major polyethylene glycol induced water stress responsive transcripts in Eucalyptus grandis using gene co-expression network. A customized array representing 3359 water stress responsive genes was designed to document their expression in leaves of E. grandis cuttings subjected to -0.225MPa of PEG treatment. The differentially expressed transcripts were documented and significantly co-expressed transcripts were used for construction of network. The co-expression network was constructed with 915 nodes and 3454 edges with degree ranging from 2 to 45. Ninety four GO categories and 117 functional pathways were identified in the network. MCODE analysis generated 27 modules and module 6 with 479 nodes and 1005 edges was identified as the biologically relevant network. The major water responsive transcripts represented in the module included dehydrin, osmotin, LEA protein, expansin, arabinogalactans, heat shock proteins, major facilitator proteins, ARM repeat proteins, raffinose synthase, tonoplast intrinsic protein and transcription factors like DREB2A, ARF9, AGL24, UNE12, WLIM1 and MYB66, MYB70, MYB 55, MYB 16 and MYB 103. The coordinated analysis of gene expression patterns and coexpression networks developed in this study identified an array of transcripts that may regulate PEG induced water stress responses in E. grandis. Copyright © 2017 Elsevier B.V. All rights reserved.

Adverse outcome pathway networks II: Network analytics.

PubMed

Villeneuve, Daniel L; Angrish, Michelle M; Fortin, Marie C; Katsiadaki, Ioanna; Leonard, Marc; Margiotta-Casaluci, Luigi; Munn, Sharon; O'Brien, Jason M; Pollesch, Nathan L; Smith, L Cody; Zhang, Xiaowei; Knapen, Dries

2018-06-01

Toxicological responses to stressors are more complex than the simple one-biological-perturbation to one-adverse-outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid in the understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present study introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using 2 example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses (or previously undefined emergent patterns of response) are introduced. Along with a companion article (part I), these concepts set the stage for the development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. The present study addresses one of the major themes identified through a Society of Environmental Toxicology and Chemistry Horizon Scanning effort focused on advancing the AOP framework. Environ Toxicol Chem 2018;37:1734-1748. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Using Module Analysis for Multiple Choice Responses: A New Method Applied to Force Concept Inventory Data

ERIC Educational Resources Information Center

Brewe, Eric; Bruun, Jesper; Bearden, Ian G.

2016-01-01

We describe "Module Analysis for Multiple Choice Responses" (MAMCR), a new methodology for carrying out network analysis on responses to multiple choice assessments. This method is used to identify modules of non-normative responses which can then be interpreted as an alternative to factor analysis. MAMCR allows us to identify conceptual…

Social Network Analysis Identifies Key Participants in Conservation Development.

PubMed

Farr, Cooper M; Reed, Sarah E; Pejchar, Liba

2018-05-01

Understanding patterns of participation in private lands conservation, which is often implemented voluntarily by individual citizens and private organizations, could improve its effectiveness at combating biodiversity loss. We used social network analysis (SNA) to examine participation in conservation development (CD), a private land conservation strategy that clusters houses in a small portion of a property while preserving the remaining land as protected open space. Using data from public records for six counties in Colorado, USA, we compared CD participation patterns among counties and identified actors that most often work with others to implement CDs. We found that social network characteristics differed among counties. The network density, or proportion of connections in the network, varied from fewer than 2 to nearly 15%, and was higher in counties with smaller populations and fewer CDs. Centralization, or the degree to which connections are held disproportionately by a few key actors, was not correlated strongly with any county characteristics. Network characteristics were not correlated with the prevalence of wildlife-friendly design features in CDs. The most highly connected actors were biological and geological consultants, surveyors, and engineers. Our work demonstrates a new application of SNA to land-use planning, in which CD network patterns are examined and key actors are identified. For better conservation outcomes of CD, we recommend using network patterns to guide strategies for outreach and information dissemination, and engaging with highly connected actor types to encourage widespread adoption of best practices for CD design and stewardship.

Multiscale Embedded Gene Co-expression Network Analysis

PubMed Central

Song, Won-Min; Zhang, Bin

2015-01-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

Multiscale Embedded Gene Co-expression Network Analysis.

PubMed

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Statistical identification of stimulus-activated network nodes in multi-neuron voltage-sensitive dye optical recordings.

PubMed

Fathiazar, Elham; Anemuller, Jorn; Kretzberg, Jutta

2016-08-01

Voltage-Sensitive Dye (VSD) imaging is an optical imaging method that allows measuring the graded voltage changes of multiple neurons simultaneously. In neuroscience, this method is used to reveal networks of neurons involved in certain tasks. However, the recorded relative dye fluorescence changes are usually low and signals are superimposed by noise and artifacts. Therefore, establishing a reliable method to identify which cells are activated by specific stimulus conditions is the first step to identify functional networks. In this paper, we present a statistical method to identify stimulus-activated network nodes as cells, whose activities during sensory network stimulation differ significantly from the un-stimulated control condition. This method is demonstrated based on voltage-sensitive dye recordings from up to 100 neurons in a ganglion of the medicinal leech responding to tactile skin stimulation. Without relying on any prior physiological knowledge, the network nodes identified by our statistical analysis were found to match well with published cell types involved in tactile stimulus processing and to be consistent across stimulus conditions and preparations.

Convergent evidence from systematic analysis of GWAS revealed genetic basis of esophageal cancer.

PubMed

Gao, Xue-Xin; Gao, Lei; Wang, Jiu-Qiang; Qu, Su-Su; Qu, Yue; Sun, Hong-Lei; Liu, Si-Dang; Shang, Ying-Li

2016-07-12

Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with risk of esophageal cancer (EC). However, investigation of genetic basis from the perspective of systematic biology and integrative genomics remains scarce.In this study, we explored genetic basis of EC based on GWAS data and implemented a series of bioinformatics methods including functional annotation, expression quantitative trait loci (eQTL) analysis, pathway enrichment analysis and pathway grouped network analysis.Two hundred and thirteen risk SNPs were identified, in which 44 SNPs were found to have significantly differential gene expression in esophageal tissues by eQTL analysis. By pathway enrichment analysis, 170 risk genes mapped by risk SNPs were enriched into 38 significant GO terms and 17 significant KEGG pathways, which were significantly grouped into 9 sub-networks by pathway grouped network analysis. The 9 groups of interconnected pathways were mainly involved with muscle cell proliferation, cellular response to interleukin-6, cell adhesion molecules, and ethanol oxidation, which might participate in the development of EC.Our findings provide genetic evidence and new insight for exploring the molecular mechanisms of EC.

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

PubMed

Fang, Weidong; Chen, Huiyue; Wang, Hansheng; Zhang, Han; Liu, Mengqi; Puneet, Munankami; Lv, Fajin; Cheng, Oumei; Wang, Xuefeng; Lu, Xiurong; Luo, Tianyou

2015-12-01

The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome. © 2015 International Parkinson and Movement Disorder Society.

Distinctive Behaviors of Druggable Proteins in Cellular Networks

PubMed Central

Workman, Paul; Al-Lazikani, Bissan

2015-01-01

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/. PMID:26699810

Synergistic Modification Induced Specific Recognition between Histone and TRIM24 via Fluctuation Correlation Network Analysis

NASA Astrophysics Data System (ADS)

Zhang, Jinmai; Luo, Huajie; Liu, Hao; Ye, Wei; Luo, Ray; Chen, Hai-Feng

2016-04-01

Histone modification plays a key role in gene regulation and gene expression. TRIM24 as a histone reader can recognize histone modification. However the specific recognition mechanism between TRIM24 and histone modification is unsolved. Here, systems biology method of dynamics correlation network based on molecular dynamics simulation was used to answer the question. Our network analysis shows that the dynamics correlation network of H3K23ac is distinctly different from that of wild type and other modifications. A hypothesis of “synergistic modification induced recognition” is then proposed to link histone modification and TRIM24 binding. These observations were further confirmed from community analysis of networks with mutation and network perturbation. Finally, a possible recognition pathway is also identified based on the shortest path search for H3K23ac. Significant difference of recognition pathway was found among different systems due to methylation and acetylation modifications. The analysis presented here and other studies show that the dynamic network-based analysis might be a useful general strategy to study the biology of protein post-translational modification and associated recognition.

Information seeking for making evidence-informed decisions: a social network analysis on the staff of a public health department in Canada

PubMed Central

2012-01-01

Background Social network analysis is an approach to study the interactions and exchange of resources among people. It can help understanding the underlying structural and behavioral complexities that influence the process of capacity building towards evidence-informed decision making. A social network analysis was conducted to understand if and how the staff of a public health department in Ontario turn to peers to get help incorporating research evidence into practice. Methods The staff were invited to respond to an online questionnaire inquiring about information seeking behavior, identification of colleague expertise, and friendship status. Three networks were developed based on the 170 participants. Overall shape, key indices, the most central people and brokers, and their characteristics were identified. Results The network analysis showed a low density and localized information-seeking network. Inter-personal connections were mainly clustered by organizational divisions; and people tended to limit information-seeking connections to a handful of peers in their division. However, recognition of expertise and friendship networks showed more cross-divisional connections. Members of the office of the Medical Officer of Health were located at the heart of the department, bridging across divisions. A small group of professional consultants and middle managers were the most-central staff in the network, also connecting their divisions to the center of the information-seeking network. In each division, there were some locally central staff, mainly practitioners, who connected their neighboring peers; but they were not necessarily connected to other experts or managers. Conclusions The methods of social network analysis were useful in providing a systems approach to understand how knowledge might flow in an organization. The findings of this study can be used to identify early adopters of knowledge translation interventions, forming Communities of Practice, and potential internal knowledge brokers. PMID:22591757

A support network typology for application in older populations with a preponderance of multigenerational households.

PubMed

Burholt, Vanessa; Dobbs, Christine

2014-08-01

This paper considers the support networks of older people in populations with a preponderance of multigenerational households and examines the most vulnerable network types in terms of loneliness and isolation. Current common typologies of support networks may not be sensitive to differences within and between different cultures. This paper uses cross-sectional data drawn from 590 elders (Gujaratis, Punjabis and Sylhetis) living in the United Kingdom and South Asia. Six variables were used in K-means cluster analysis to establish a new network typology. Two logistic regression models using loneliness and isolation as dependent variables assessed the contribution of the new network type to wellbeing. Four support networks were identified: 'Multigenerational Households: Older Integrated Networks', 'Multigenerational Households: Younger Family Networks', 'Family and Friends Integrated Networks' and 'Non-kin Restricted Networks'. Older South Asians with 'Non-kin Restricted Networks' were more likely to be lonely and isolated compared to others. Using network typologies developed with individualistically oriented cultures, distributions are skewed towards more robust network types and could underestimate the support needs of older people from familistic cultures, who may be isolated and lonely and with limited informal sources of help. The new typology identifies different network types within multigenerational households, identifies a greater proportion of older people with vulnerable networks and could positively contribute to service planning.

Analysing published global Ebola Virus Disease research using social network analysis

PubMed Central

Hagel, Christiane; Weidemann, Felix; Gauch, Stephan; Edwards, Suzanne

2017-01-01

Introduction The 2014/2015 West African Ebola Virus Disease (EVD) outbreak attracted global attention. Numerous opinions claimed that the global response was impaired, in part because, the EVD research was neglected, although quantitative or qualitative studies did not exist. Our objective was to analyse how the EVD research landscape evolved by exploring the existing research network and its communities before and during the outbreak in West Africa. Methods/ Principal findings Social network analysis (SNA) was used to analyse collaborations between institutions named by co-authors as affiliations in publications on EVD. Bibliometric data of publications on EVD between 1976 and 2015 was collected from Thomson Reuters’ Web of Science Core Collection (WoS). Freely available software was used for network analysis at a global-level and for 10-year periods. The networks are presented as undirected-weighted graphs. Rankings by degree and betweenness were calculated to identify central and powerful network positions; modularity function was used to identify research communities. Overall 4,587 publications were identified, of which 2,528 were original research articles. Those yielded 1,644 authors’ affiliated institutions and 9,907 connections for co-authorship network construction. The majority of institutions were from the USA, Canada and Europe. Collaborations with research partners on the African continent did exist, but less frequently. Around six highly connected organisations in the network were identified with powerful and broker positions. Network characteristics varied widely among the 10-year periods and evolved from 30 to 1,489 institutions and 60 to 9,176 connections respectively. Most influential actors are from public or governmental institutions whereas private sector actors, in particular the pharmaceutical industry, are largely absent. Conclusion/ Significance Research output on EVD has increased over time and surged during the 2014/2015 outbreak. The overall EVD research network is organised around a few key actors, signalling a concentration of expertise but leaving room for increased cooperation with other institutions especially from affected countries. Finding innovative ways to maintain support for these pivotal actors while steering the global EVD research network towards an agenda driven by agreed, prioritized needs and finding ways to better integrate currently peripheral and newer expertise may accelerate the translation of research into the development of necessary live saving products for EVD ahead of the next outbreak. PMID:28991915

Analysing published global Ebola Virus Disease research using social network analysis.

PubMed

Hagel, Christiane; Weidemann, Felix; Gauch, Stephan; Edwards, Suzanne; Tinnemann, Peter

2017-10-01

The 2014/2015 West African Ebola Virus Disease (EVD) outbreak attracted global attention. Numerous opinions claimed that the global response was impaired, in part because, the EVD research was neglected, although quantitative or qualitative studies did not exist. Our objective was to analyse how the EVD research landscape evolved by exploring the existing research network and its communities before and during the outbreak in West Africa. Social network analysis (SNA) was used to analyse collaborations between institutions named by co-authors as affiliations in publications on EVD. Bibliometric data of publications on EVD between 1976 and 2015 was collected from Thomson Reuters' Web of Science Core Collection (WoS). Freely available software was used for network analysis at a global-level and for 10-year periods. The networks are presented as undirected-weighted graphs. Rankings by degree and betweenness were calculated to identify central and powerful network positions; modularity function was used to identify research communities. Overall 4,587 publications were identified, of which 2,528 were original research articles. Those yielded 1,644 authors' affiliated institutions and 9,907 connections for co-authorship network construction. The majority of institutions were from the USA, Canada and Europe. Collaborations with research partners on the African continent did exist, but less frequently. Around six highly connected organisations in the network were identified with powerful and broker positions. Network characteristics varied widely among the 10-year periods and evolved from 30 to 1,489 institutions and 60 to 9,176 connections respectively. Most influential actors are from public or governmental institutions whereas private sector actors, in particular the pharmaceutical industry, are largely absent. Research output on EVD has increased over time and surged during the 2014/2015 outbreak. The overall EVD research network is organised around a few key actors, signalling a concentration of expertise but leaving room for increased cooperation with other institutions especially from affected countries. Finding innovative ways to maintain support for these pivotal actors while steering the global EVD research network towards an agenda driven by agreed, prioritized needs and finding ways to better integrate currently peripheral and newer expertise may accelerate the translation of research into the development of necessary live saving products for EVD ahead of the next outbreak.

Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis.

PubMed

Lee, A Yeong; Park, Won; Kang, Tae-Wook; Cha, Min Ho; Chun, Jin Mi

2018-07-15

Yijin-Tang (YJT) is a traditional prescription for the treatment of hyperlipidaemia, atherosclerosis and other ailments related to dampness phlegm, a typical pathological symptom of abnormal body fluid metabolism in Traditional Korean Medicine. However, a holistic network pharmacology approach to understanding the therapeutic mechanisms underlying hyperlipidaemia and atherosclerosis has not been pursued. To examine the network pharmacological potential effects of YJT on hyperlipidaemia and atherosclerosis, we analysed components, performed target prediction and network analysis, and investigated interacting pathways using a network pharmacology approach. Information on compounds in herbal medicines was obtained from public databases, and oral bioavailability and drug-likeness was screened using absorption, distribution, metabolism, and excretion (ADME) criteria. Correlations between compounds and genes were linked using the STITCH database, and genes related to hyperlipidaemia and atherosclerosis were gathered using the GeneCards database. Human genes were identified and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Network analysis identified 447 compounds in five herbal medicines that were subjected to ADME screening, and 21 compounds and 57 genes formed the main pathways linked to hyperlipidaemia and atherosclerosis. Among them, 10 compounds (naringenin, nobiletin, hesperidin, galangin, glycyrrhizin, homogentisic acid, stigmasterol, 6-gingerol, quercetin and glabridin) were linked to more than four genes, and are bioactive compounds and key chemicals. Core genes in this network were CASP3, CYP1A1, CYP1A2, MMP2 and MMP9. The compound-target gene network revealed close interactions between multiple components and multiple targets, and facilitates a better understanding of the potential therapeutic effects of YJT. Pharmacological network analysis can help to explain the potential effects of YJT for treating dampness phlegm-related diseases such as hyperlipidaemia and atherosclerosis. Copyright © 2018 Elsevier B.V. All rights reserved.

[Reliability theory based on quality risk network analysis for Chinese medicine injection].

PubMed

Li, Zheng; Kang, Li-Yuan; Fan, Xiao-Hui

2014-08-01

A new risk analysis method based upon reliability theory was introduced in this paper for the quality risk management of Chinese medicine injection manufacturing plants. The risk events including both cause and effect ones were derived in the framework as nodes with a Bayesian network analysis approach. It thus transforms the risk analysis results from failure mode and effect analysis (FMEA) into a Bayesian network platform. With its structure and parameters determined, the network can be used to evaluate the system reliability quantitatively with probabilistic analytical appraoches. Using network analysis tools such as GeNie and AgenaRisk, we are able to find the nodes that are most critical to influence the system reliability. The importance of each node to the system can be quantitatively evaluated by calculating the effect of the node on the overall risk, and minimization plan can be determined accordingly to reduce their influences and improve the system reliability. Using the Shengmai injection manufacturing plant of SZYY Ltd as a user case, we analyzed the quality risk with both static FMEA analysis and dynamic Bayesian Network analysis. The potential risk factors for the quality of Shengmai injection manufacturing were identified with the network analysis platform. Quality assurance actions were further defined to reduce the risk and improve the product quality.

A Study for the Feature Selection to Identify GIEMSA-Stained Human Chromosomes Based on Artificial Neural Network

DTIC Science & Technology

2001-10-25

neural network (ANN) has been adopted for the human chromosome classification. It is important to select optimum features for training neural network...Many studies for computer-based chromosome analysis have shown that it is possible to classify chromosomes into 24 subgroups. In addition, artificial

Identifying apple surface defects using principal components analysis and artifical neural networks

USDA-ARS?s Scientific Manuscript database

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Meta-Analysis of Genome-Wide Association Studies and Network Analysis-Based Integration with Gene Expression Data Identify New Suggestive Loci and Unravel a Wnt-Centric Network Associated with Dupuytren’s Disease

PubMed Central

Becker, Kerstin; Siegert, Sabine; Toliat, Mohammad Reza; Du, Juanjiangmeng; Casper, Ramona; Dolmans, Guido H.; Werker, Paul M.; Tinschert, Sigrid; Franke, Andre; Gieger, Christian; Strauch, Konstantin; Nothnagel, Michael; Nürnberg, Peter; Hennies, Hans Christian

2016-01-01

Dupuytren´s disease, a fibromatosis of the connective tissue in the palm, is a common complex disease with a strong genetic component. Up to date nine genetic loci have been found to be associated with the disease. Six of these loci contain genes that code for Wnt signalling proteins. In spite of this striking first insight into the genetic factors in Dupuytren´s disease, much of the inherited risk in Dupuytren´s disease still needs to be discovered. The already identified loci jointly explain ~1% of the heritability in this disease. To further elucidate the genetic basis of Dupuytren´s disease, we performed a genome-wide meta-analysis combining three genome-wide association study (GWAS) data sets, comprising 1,580 cases and 4,480 controls. We corroborated all nine previously identified loci, six of these with genome-wide significance (p-value < 5x10-8). In addition, we identified 14 new suggestive loci (p-value < 10−5). Intriguingly, several of these new loci contain genes associated with Wnt signalling and therefore represent excellent candidates for replication. Next, we compared whole-transcriptome data between patient- and control-derived tissue samples and found the Wnt/β-catenin pathway to be the top deregulated pathway in patient samples. We then conducted network and pathway analyses in order to identify protein networks that are enriched for genes highlighted in the GWAS meta-analysis and expression data sets. We found further evidence that the Wnt signalling pathways in conjunction with other pathways may play a critical role in Dupuytren´s disease. PMID:27467239

Social Network Type and Long-Term Condition Management Support: A Cross-Sectional Study in Six European Countries.

PubMed

Vassilev, Ivaylo; Rogers, Anne; Kennedy, Anne; Wensing, Michel; Koetsenruijter, Jan; Orlando, Rosanna; Portillo, Maria Carmen; Culliford, David

2016-01-01

Network types and characteristics have been linked to the capacity of inter-personal environments to mobilise and share resources. The aim of this paper is to examine personal network types in relation to long-term condition management in order to identify the properties of network types most likely to provide support for those with a long-term condition. A cross-sectional observational survey of people with type 2 diabetes using interviews and questionnaires was conducted between April and October 2013 in six European countries: Greece, Spain, Bulgaria, Norway, United Kingdom, and Netherlands. 1862 people with predominantly lower socio-economic status were recruited from each country. We used k-means clustering analysis to derive the network types, and one-way analysis of variance and multivariate logistic regression analysis to explore the relationship between network type socio-economic characteristics, self-management monitoring and skills, well-being, and network member work. Five network types of people with long-term conditions were identified: restricted, minimal family, family, weak ties, and diverse. Restricted network types represented those with the poorest self-management skills and were associated with limited support from social network members. Restricted networks were associated with poor indicators across self-management capacity, network support, and well-being. Diverse networks were associated with more enhanced self-management skills amongst those with a long-term condition and high level of emotional support. It was the three network types which had a large number of network members (diverse, weak ties, and family) where healthcare utilisation was most likely to correspond to existing health needs. Our findings suggest that type of increased social involvement is linked to greater self-management capacity and potentially lower formal health care costs indicating that diverse networks constitute the optimal network type as a policy in terms of the design of LTCM interventions and building support for people with LTCs.

A Co-Citation Network of Young Children's Learning with Technology

ERIC Educational Resources Information Center

Tang, Kai-Yu; Li, Ming-Chaun; Hsin, Ching-Ting; Tsai, Chin-Chung

2016-01-01

This paper used a novel literature review approach--co-citation network analysis--to illuminate the latent structure of 87 empirical papers in the field of young children's learning with technology (YCLT). Based on the document co-citation analysis, a total of 206 co-citation relationships among the 87 papers were identified and then graphically…

Identifying major depressive disorder using Hurst exponent of resting-state brain networks.

PubMed

Wei, Maobin; Qin, Jiaolong; Yan, Rui; Li, Haoran; Yao, Zhijian; Lu, Qing

2013-12-30

Resting-state functional magnetic resonance imaging (fMRI) studies of major depressive disorder (MDD) have revealed abnormalities of functional connectivity within or among the resting-state networks. They provide valuable insight into the pathological mechanisms of depression. However, few reports were involved in the "long-term memory" of fMRI signals. This study was to investigate the "long-term memory" of resting-state networks by calculating their Hurst exponents for identifying depressed patients from healthy controls. Resting-state networks were extracted from fMRI data of 20 MDD and 20 matched healthy control subjects. The Hurst exponent of each network was estimated by Range Scale analysis for further discriminant analysis. 95% of depressed patients and 85% of healthy controls were correctly classified by Support Vector Machine with an accuracy of 90%. The right fronto-parietal and default mode network constructed a deficit network (lower memory and more irregularity in MDD), while the left fronto-parietal, ventromedial prefrontal and salience network belonged to an excess network (longer memory in MDD), suggesting these dysfunctional networks may be related to a portion of the complex of emotional and cognitive disturbances. The abnormal "long-term memory" of resting-state networks associated with depression may provide a new possibility towards the exploration of the pathophysiological mechanisms of MDD. © 2013 Elsevier Ireland Ltd. All rights reserved.

Analyzing big data in social media: Text and network analyses of an eating disorder forum.

PubMed

Moessner, Markus; Feldhege, Johannes; Wolf, Markus; Bauer, Stephanie

2018-05-10

Social media plays an important role in everyday life of young people. Numerous studies claim negative effects of social media and media in general on eating disorder risk factors. Despite the availability of big data, only few studies have exploited the possibilities so far in the field of eating disorders. Methods for data extraction, computerized content analysis, and network analysis will be introduced. Strategies and methods will be exemplified for an ad-hoc dataset of 4,247 posts and 34,118 comments by 3,029 users of the proed forum on Reddit. Text analysis with latent Dirichlet allocation identified nine topics related to social support and eating disorder specific content. Social network analysis describes the overall communication patterns, and could identify community structures and most influential users. A linear network autocorrelation model was applied to estimate associations in language among network neighbors. The supplement contains R code for data extraction and analyses. This paper provides an introduction to investigating social media data, and will hopefully stimulate big data social media research in eating disorders. When applied in real-time, the methods presented in this manuscript could contribute to improving the safety of ED-related online communication. © 2018 Wiley Periodicals, Inc.

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Network structure and travel time perception.

PubMed

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

Identification of hybrid node and link communities in complex networks

PubMed Central

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-01-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

Identification of hybrid node and link communities in complex networks.

PubMed

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-02

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Identification of hybrid node and link communities in complex networks

NASA Astrophysics Data System (ADS)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Novel Loci for Metabolic Networks and Multi-Tissue Expression Studies Reveal Genes for Atherosclerosis

PubMed Central

Inouye, Michael; Ripatti, Samuli; Kettunen, Johannes; Lyytikäinen, Leo-Pekka; Oksala, Niku; Laurila, Pirkka-Pekka; Kangas, Antti J.; Soininen, Pasi; Savolainen, Markku J.; Viikari, Jorma; Kähönen, Mika; Perola, Markus; Salomaa, Veikko; Raitakari, Olli; Lehtimäki, Terho; Taskinen, Marja-Riitta; Järvelin, Marjo-Riitta; Ala-Korpela, Mika; Palotie, Aarno; de Bakker, Paul I. W.

2012-01-01

Association testing of multiple correlated phenotypes offers better power than univariate analysis of single traits. We analyzed 6,600 individuals from two population-based cohorts with both genome-wide SNP data and serum metabolomic profiles. From the observed correlation structure of 130 metabolites measured by nuclear magnetic resonance, we identified 11 metabolic networks and performed a multivariate genome-wide association analysis. We identified 34 genomic loci at genome-wide significance, of which 7 are novel. In comparison to univariate tests, multivariate association analysis identified nearly twice as many significant associations in total. Multi-tissue gene expression studies identified variants in our top loci, SERPINA1 and AQP9, as eQTLs and showed that SERPINA1 and AQP9 expression in human blood was associated with metabolites from their corresponding metabolic networks. Finally, liver expression of AQP9 was associated with atherosclerotic lesion area in mice, and in human arterial tissue both SERPINA1 and AQP9 were shown to be upregulated (6.3-fold and 4.6-fold, respectively) in atherosclerotic plaques. Our study illustrates the power of multi-phenotype GWAS and highlights candidate genes for atherosclerosis. PMID:22916037

Scientometric methods for identifying emerging technologies

DOEpatents

Abercrombie, Robert K; Schlicher, Bob G; Sheldon, Frederick T

2015-11-03

Provided is a method of generating a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, worldwide patents, news archives, and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain.

A latent class analysis of friendship network types and their predictors in the second half of life.

PubMed

Miche, Martina; Huxhold, Oliver; Stevens, Nan L

2013-07-01

Friendships contribute uniquely to well-being in (late) adulthood. However, studies on friendship often ignore interindividual differences in friendship patterns. The aim of this study was to investigate such differences including their predictors. The study builds on Matthews's qualitative model of friendship styles. Matthews distinguished 3 approaches to friendship differing by number of friends, duration of friendships, and emotional closeness. We used latent class analysis to identify friendship network types in a sample of middle-aged and older adults aged 40-85 years (N = 1,876). Data came from the German Aging Survey (DEAS). Our analysis revealed 4 distinct friendship network types that were in high congruence with Matthews's typology. We identified these as a discerning style, which focuses on few close relationships, an independent style, which refrains from close engagements, and 2 acquisitive styles that both acquire new friends across their whole life course but differ regarding the emotional closeness of their friendships. Socioeconomic status, gender, health, and network-disturbing and network-sustaining variables predicted affiliations with network types. We argue that future studies should consider a holistic view of friendships in order to better understand the association between friendships and well-being in the second half of life.

Text mining and network analysis to find functional associations of genes in high altitude diseases.

PubMed

Bhasuran, Balu; Subramanian, Devika; Natarajan, Jeyakumar

2018-05-02

Travel to elevations above 2500 m is associated with the risk of developing one or more forms of acute altitude illness such as acute mountain sickness (AMS), high altitude cerebral edema (HACE) or high altitude pulmonary edema (HAPE). Our work aims to identify the functional association of genes involved in high altitude diseases. In this work we identified the gene networks responsible for high altitude diseases by using the principle of gene co-occurrence statistics from literature and network analysis. First, we mined the literature data from PubMed on high-altitude diseases, and extracted the co-occurring gene pairs. Next, based on their co-occurrence frequency, gene pairs were ranked. Finally, a gene association network was created using statistical measures to explore potential relationships. Network analysis results revealed that EPO, ACE, IL6 and TNF are the top five genes that were found to co-occur with 20 or more genes, while the association between EPAS1 and EGLN1 genes is strongly substantiated. The network constructed from this study proposes a large number of genes that work in-toto in high altitude conditions. Overall, the result provides a good reference for further study of the genetic relationships in high altitude diseases. Copyright © 2018 Elsevier Ltd. All rights reserved.

Characterizing Cancer Drug Response and Biological Correlates: A Geometric Network Approach.

PubMed

Pouryahya, Maryam; Oh, Jung Hun; Mathews, James C; Deasy, Joseph O; Tannenbaum, Allen R

2018-04-23

In the present work, we apply a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure, via a link between Ricci curvature and network robustness established by the theory of optimal mass transport, the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For genes identified as important, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in biological processes associated with drug response across cell lines and tissue types which are plausible from the point of view of the biological literature. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.

Social networks of older adults living with HIV in Finland.

PubMed

Nobre, Nuno Ribeiro; Kylmä, Jari; Kirsi, Tapio; Pereira, Marco

2016-01-01

The aim of this study was to explore the social networks of older adults living with HIV. Interviews were conducted with nine individuals aged 50 or older living with HIV in Helsinki, Finland. Analysis of transcripts was analysed by inductive qualitative content analysis. Results indicated that these participants' networks tended to be large, including those both aware and unaware of the participants' health status. Analysis identified three main themes: large multifaceted social networks, importance of a support group, and downsizing of social networks. Support received appeared to be of great importance in coping with their health condition, especially since the time of diagnosis. Friends and family were the primary source of informal support. The majority of participants relied mostly on friends, some of whom were HIV-positive. Formal support came primarily from the HIV organisation's support group. In this study group, non-disclosure did not impact participants' well-being. In years to come, social networks of older adults living with HIV may shrink due to personal reasons other than HIV-disclosure. What is of primary importance is that healthcare professionals become knowledgeable about psychosocial issues of older adults living with HIV, identifying latent problems and developing adequate interventions in the early stages of the disease; this would help prevent social isolation and foster successful ageing with HIV.

Global Electricity Trade Network: Structures and Implications

PubMed Central

Ji, Ling; Jia, Xiaoping; Chiu, Anthony S. F.; Xu, Ming

2016-01-01

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions. PMID:27504825

Global Electricity Trade Network: Structures and Implications.

PubMed

Ji, Ling; Jia, Xiaoping; Chiu, Anthony S F; Xu, Ming

2016-01-01

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions.

Predicting missing links and identifying spurious links via likelihood analysis

NASA Astrophysics Data System (ADS)

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-03-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.

Predicting missing links and identifying spurious links via likelihood analysis

PubMed Central

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-01-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. PMID:26961965

Z-Score-Based Modularity for Community Detection in Networks

PubMed Central

Miyauchi, Atsushi; Kawase, Yasushi

2016-01-01

Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given partition with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function. PMID:26808270

Spatial correlation analysis of urban traffic state under a perspective of community detection

NASA Astrophysics Data System (ADS)

Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

2018-05-01

Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

PubMed

Ammenwerth, Elske; Hackl, Werner O

2017-01-01

Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

PubMed

Suratanee, Apichat; Plaimas, Kitiporn

2017-01-01

The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k -nearest neighbor (R k NN) search. The R k NN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the R k NN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

Ecological network analysis for a virtual water network.

PubMed

Fang, Delin; Chen, Bin

2015-06-02

The notions of virtual water flows provide important indicators to manifest the water consumption and allocation between different sectors via product transactions. However, the configuration of virtual water network (VWN) still needs further investigation to identify the water interdependency among different sectors as well as the network efficiency and stability in a socio-economic system. Ecological network analysis is chosen as a useful tool to examine the structure and function of VWN and the interactions among its sectors. A balance analysis of efficiency and redundancy is also conducted to describe the robustness (RVWN) of VWN. Then, network control analysis and network utility analysis are performed to investigate the dominant sectors and pathways for virtual water circulation and the mutual relationships between pairwise sectors. A case study of the Heihe River Basin in China shows that the balance between efficiency and redundancy is situated on the left side of the robustness curve with less efficiency and higher redundancy. The forestation, herding and fishing sectors and industrial sectors are found to be the main controllers. The network tends to be more mutualistic and synergic, though some competitive relationships that weaken the virtual water circulation still exist.

Distinctive fingerprints of erosional regimes in terrestrial channel networks

NASA Astrophysics Data System (ADS)

Grau Galofre, A.; Jellinek, M.

2017-12-01

Satellite imagery and digital elevation maps capture the large scale morphology of channel networks attributed to long term erosional processes, such as fluvial, glacial, groundwater sapping and subglacial erosion. Characteristic morphologies associated with each of these styles of erosion have been studied in detail, but there exists a knowledge gap related to their parameterization and quantification. This knowledge gap prevents a rigorous analysis of the dominant processes that shaped a particular landscape, and a comparison across styles of erosion. To address this gap, we use previous morphological descriptions of glaciers, rivers, sapping valleys and tunnel valleys to identify and measure quantitative metrics diagnostic of these distinctive styles of erosion. From digital elevation models, we identify four geometric metrics: The minimum channel width, channel aspect ratio (longest length to channel width at the outlet), presence of undulating longitudinal profiles, and tributary junction angle. We also parameterize channel network complexity in terms of its stream order and fractal dimension. We then perform a statistical classification of the channel networks using a Principal Component Analysis on measurements of these six metrics on a dataset of 70 channelized systems. We show that rivers, glaciers, groundwater seepage and subglacial meltwater erode the landscape in rigorously distinguishable ways. Our methodology can more generally be applied to identify the contributions of different processes involved in carving a channel network. In particular, we are able to identify transitions from fluvial to glaciated landscapes or vice-versa.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.

PubMed

Tibau, Elisenda; Valencia, Miguel; Soriano, Jordi

2013-01-01

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis.

PubMed

van Dam, Jesse C J; Schaap, Peter J; Martins dos Santos, Vitor A P; Suárez-Diez, María

2014-09-26

Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each separate set has an intrinsic value that is diluted and partly lost when building a consensus network. Here we present a methodology to generate co-expression networks and, instead of a consensus network, we propose an integration framework where the different networks are kept and analysed with additional tools to efficiently combine the information extracted from each network. We developed a workflow to efficiently analyse information generated by different inference and prediction methods. Our methodology relies on providing the user the means to simultaneously visualise and analyse the coexisting networks generated by different algorithms, heterogeneous datasets, and a suite of analysis tools. As a show case, we have analysed the gene co-expression networks of Mycobacterium tuberculosis generated using over 600 expression experiments. Regarding DNA damage repair, we identified SigC as a key control element, 12 new targets for LexA, an updated LexA binding motif, and a potential mismatch repair system. We expanded the DevR regulon with 27 genes while identifying 9 targets wrongly assigned to this regulon. We discovered 10 new genes linked to zinc uptake and a new regulatory mechanism for ZuR. The use of co-expression networks to perform system level analysis allows the development of custom made methodologies. As show cases we implemented a pipeline to integrate ChIP-seq data and another method to uncover multiple regulatory layers. Our workflow is based on representing the multiple types of information as network representations and presenting these networks in a synchronous framework that allows their simultaneous visualization while keeping specific associations from the different networks. By simultaneously exploring these networks and metadata, we gained insights into regulatory mechanisms in M. tuberculosis that could not be obtained through the separate analysis of each data type.

A method for independent component graph analysis of resting-state fMRI.

PubMed

Ribeiro de Paula, Demetrius; Ziegler, Erik; Abeyasinghe, Pubuditha M; Das, Tushar K; Cavaliere, Carlo; Aiello, Marco; Heine, Lizette; di Perri, Carol; Demertzi, Athena; Noirhomme, Quentin; Charland-Verville, Vanessa; Vanhaudenhuyse, Audrey; Stender, Johan; Gomez, Francisco; Tshibanda, Jean-Flory L; Laureys, Steven; Owen, Adrian M; Soddu, Andrea

2017-03-01

Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness. This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

Health and disease phenotyping in old age using a cluster network analysis.

PubMed

Valenzuela, Jesus Felix; Monterola, Christopher; Tong, Victor Joo Chuan; Ng, Tze Pin; Larbi, Anis

2017-11-15

Human ageing is a complex trait that involves the synergistic action of numerous biological processes that interact to form a complex network. Here we performed a network analysis to examine the interrelationships between physiological and psychological functions, disease, disability, quality of life, lifestyle and behavioural risk factors for ageing in a cohort of 3,270 subjects aged ≥55 years. We considered associations between numerical and categorical descriptors using effect-size measures for each variable pair and identified clusters of variables from the resulting pairwise effect-size network and minimum spanning tree. We show, by way of a correspondence analysis between the two sets of clusters, that they correspond to coarse-grained and fine-grained structure of the network relationships. The clusters obtained from the minimum spanning tree mapped to various conceptual domains and corresponded to physiological and syndromic states. Hierarchical ordering of these clusters identified six common themes based on interactions with physiological systems and common underlying substrates of age-associated morbidity and disease chronicity, functional disability, and quality of life. These findings provide a starting point for indepth analyses of ageing that incorporate immunologic, metabolomic and proteomic biomarkers, and ultimately offer low-level-based typologies of healthy and unhealthy ageing.

Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections

PubMed Central

Lipner, Ettie M.; Garcia, Benjamin J.; Strong, Michael

2016-01-01

Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects. PMID:26751573

Quantitative methods of identifying the key nodes in the illegal wildlife trade network

PubMed Central

Patel, Nikkita Gunvant; Rorres, Chris; Joly, Damien O.; Brownstein, John S.; Boston, Ray; Levy, Michael Z.; Smith, Gary

2015-01-01

Innovative approaches are needed to combat the illegal trade in wildlife. Here, we used network analysis and a new database, HealthMap Wildlife Trade, to identify the key nodes (countries) that support the illegal wildlife trade. We identified key exporters and importers from the number of shipments a country sent and received and from the number of connections a country had to other countries over a given time period. We used flow betweenness centrality measurements to identify key intermediary countries. We found the set of nodes whose removal from the network would cause the maximum disruption to the network. Selecting six nodes would fragment 89.5% of the network for elephants, 92.3% for rhinoceros, and 98.1% for tigers. We then found sets of nodes that would best disseminate an educational message via direct connections through the network. We would need to select 18 nodes to reach 100% of the elephant trade network, 16 nodes for rhinoceros, and 10 for tigers. Although the choice of locations for interventions should be customized for the animal and the goal of the intervention, China was the most frequently selected country for network fragmentation and information dissemination. Identification of key countries will help strategize illegal wildlife trade interventions. PMID:26080413

A Formal Analysis of Cytokine Networks in Chronic Fatigue Syndrome

PubMed Central

Broderick, Gordon; Fuite, Jim; Kreitz, Andrea; Vernon, Suzanne D; Klimas, Nancy; Fletcher, Mary Ann

2010-01-01

Chronic Fatigue Syndrome (CFS) is a complex illness affecting 4 million Americans for which no characteristic lesion has been identified. Instead of searching for a deficiency in any single marker, we propose that CFS is associated with a profound imbalance in the regulation of immune function forcing a departure from standard preprogrammed responses. To identify these imbalances we apply network analysis to the co-expression of 16 cytokines in CFS subjects and healthy controls. Concentrations of IL-1a, 1b, 2, 4, 5, 6, 8, 10, 12, 13, 15, 17 and 23, IFN-γ, lymphotoxin-α (LT-α) and TNF-α were measured in the plasma of 40 female CFS and 59 case-matched controls. Cytokine co-expression networks were constructed from the pair-wise mutual information (MI) patterns found within each subject group. These networks differed in topology significantly more than expected by chance with the CFS network being more hub-like in design. Analysis of local modularity isolated statistically distinct cytokine communities recognizable as pre-programmed immune functional components. These showed highly attenuated Th1 and Th17 immune responses in CFS. High Th2 marker expression but weak interaction patterns pointed to an established Th2 inflammatory milieu. Similarly, altered associations in CFS provided indirect evidence of diminished NK cell responsiveness to IL-12 and LTα stimulus. These observations are consistent with several processes active in latent viral infection and would not have been uncovered by assessing marker expression alone. Furthermore this analysis identifies key subnetworks such as IL-2:IFNγ:TNFα that might be targeted in restoring normal immune function. PMID:20447453

Predicting and Detecting Emerging Cyberattack Patterns Using StreamWorks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chin, George; Choudhury, Sutanay; Feo, John T.

2014-06-30

The number and sophistication of cyberattacks on industries and governments have dramatically grown in recent years. To counter this movement, new advanced tools and techniques are needed to detect cyberattacks in their early stages such that defensive actions may be taken to avert or mitigate potential damage. From a cybersecurity analysis perspective, detecting cyberattacks may be cast as a problem of identifying patterns in computer network traffic. Logically and intuitively, these patterns may take on the form of a directed graph that conveys how an attack or intrusion propagates through the computers of a network. Such cyberattack graphs could providemore » cybersecurity analysts with powerful conceptual representations that are natural to express and analyze. We have been researching and developing graph-centric approaches and algorithms for dynamic cyberattack detection. The advanced dynamic graph algorithms we are developing will be packaged into a streaming network analysis framework known as StreamWorks. With StreamWorks, a scientist or analyst may detect and identify precursor events and patterns as they emerge in complex networks. This analysis framework is intended to be used in a dynamic environment where network data is streamed in and is appended to a large-scale dynamic graph. Specific graphical query patterns are decomposed and collected into a graph query library. The individual decomposed subpatterns in the library are continuously and efficiently matched against the dynamic graph as it evolves to identify and detect early, partial subgraph patterns. The scalable emerging subgraph pattern algorithms will match on both structural and semantic network properties.« less

Transnational Research Networks in Chinese Scientific Production. An Investigation on Health-Industry Related Sectors.

PubMed

Rubini, Lauretta; Pollio, Chiara; Di Tommaso, Marco R

2017-08-29

Transnational research networks (TRN) are becoming increasingly complex. Such complexity may have both positive and negative effects on the quality of research. Our work studies the evolution over time of Chinese TRN and the role of complexity on the quality of Chinese research, given the leading role this country has recently acquired in international science. We focus on the fields of geriatrics and gerontology. We build an original dataset of all scientific publications of China in these areas in 2009, 2012 and 2015, starting from the ISI Web of Knowledge (ISI WoK) database. Using Social Network Analysis (SNA), we analyze the change in scientific network structure across time. Second, we design indices to control for the different aspects of networks complexity (number of authors, country heterogeneity and institutional heterogeneity) and we perform negative binomial regressions to identify the main determinants of research quality. Our analysis shows that research networks in the field of geriatrics and gerontology have gradually become wider in terms of countries and have become more balanced. Furthermore, our results identify that different forms of complexity have different impacts on quality, including a reciprocal moderating effect. In particular, according to our analysis, research quality benefits from complex research networks both in terms of countries and of types of institutions involved, but that such networks should be "compact" in terms of number of authors. Eventually, we suggest that complexity should be carefully taken into account when designing policies aimed at enhancing the quality of research.

Stream network analysis and geomorphic flood plain mapping from orbital and suborbital remote sensing imagery application to flood hazard studies in central Texas

NASA Technical Reports Server (NTRS)

Baker, V. R. (Principal Investigator); Holz, R. K.; Hulke, S. D.; Patton, P. C.; Penteado, M. M.

1975-01-01

The author has identified the following significant results. Development of a quantitative hydrogeomorphic approach to flood hazard evaluation was hindered by (1) problems of resolution and definition of the morphometric parameters which have hydrologic significance, and (2) mechanical difficulties in creating the necessary volume of data for meaningful analysis. Measures of network resolution such as drainage density and basin Shreve magnitude indicated that large scale topographic maps offered greater resolution than small scale suborbital imagery and orbital imagery. The disparity in network resolution capabilities between orbital and suborbital imagery formats depends on factors such as rock type, vegetation, and land use. The problem of morphometric data analysis was approached by developing a computer-assisted method for network analysis. The system allows rapid identification of network properties which can then be related to measures of flood response.

The Art of Athlete Leadership: Identifying High-Quality Athlete Leadership at the Individual and Team Level Through Social Network Analysis.

PubMed

Fransen, Katrien; Van Puyenbroeck, Stef; Loughead, Todd M; Vanbeselaere, Norbert; De Cuyper, Bert; Vande Broek, Gert; Boen, Filip

2015-06-01

This research aimed to introduce social network analysis as a novel technique in sports teams to identify the attributes of high-quality athlete leadership, both at the individual and at the team level. Study 1 included 25 sports teams (N = 308 athletes) and focused on athletes' general leadership quality. Study 2 comprised 21 sports teams (N = 267 athletes) and focused on athletes' specific leadership quality as a task, motivational, social, and external leader. The extent to which athletes felt connected with their leader proved to be most predictive for athletes' perceptions of that leader's quality on each leadership role. Also at the team level, teams with higher athlete leadership quality were more strongly connected. We conclude that social network analysis constitutes a valuable tool to provide more insight in the attributes of high-quality leadership both at the individual and at the team level.

Identification of potential opinion leaders in child health promotion in Sweden using network analysis.

PubMed

Guldbrandsson, Karin; Nordvik, Monica K; Bremberg, Sven

2012-08-08

Opinion leaders are often local individuals with high credibility who can influence other people. Robust effects using opinion leaders in diffusing innovations have been shown in several randomized controlled trials, for example regarding sexually transmitted infections (STI), human immunodeficiency virus (HIV) prevention, mammography rates and caesarean birth delivery rates. In a Cochrane review 2010 it was concluded that the use of opinion leaders can successfully promote evidence-based practice. Thus, using opinion leaders within the public health sector might be one means to speed up the dissemination of health promoting and disease preventing innovations. Social network analysis has been used to trace and map networks, with focus on relationships and positions, in widely spread arenas and topics. The purpose of this study was to use social network analysis in order to identify potential opinion leaders at the arena of child health promotion in Sweden. By using snowball technique a short e-mail question was spread in up to five links, starting from seven initially invited persons. This inquiry resulted in a network consisting of 153 individuals. The most often mentioned actors were researchers, public health officials and paediatricians, or a combination of these professions. Four single individuals were mentioned by five to seven other persons in the network. These individuals obviously possess qualities that make other professionals within the public health sector listen to and trust them. Social network analysis seemed to be a useful method to identify influential persons with high credibility, i.e. potential opinion leaders, at the arena of child health promotion in Sweden. If genuine opinion leaders could be identified directed measures can be carried out in order to spread new and relevant knowledge. This may facilitate for public health actors at the local, regional and national level to more rapidly progress innovations into everyday practice. However, effectiveness studies of opinion leaders in the public health sector still have to be performed.

Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways

PubMed Central

Jo, Kyuri; Jung, Inuk; Moon, Ji Hwan; Kim, Sun

2016-01-01

Motivation: To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools. Most pathway analysis methods are not designed for time series data and they do not consider gene-gene influence on the time dimension. Results: In this article, we propose a novel time-series analysis method TimeTP for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein–protein interaction network to locate TFs triggering the perturbation. TimeTP first identifies perturbed sub-pathways that propagate the expression changes along the time. Starting points of the perturbed sub-pathways are mapped into the network and the most influential TFs are determined by influence maximization technique. The analysis result is visually summarized in TF-Pathway map in time clock. TimeTP was applied to PIK3CA knock-in dataset and found significant sub-pathways and their regulators relevant to the PIP3 signaling pathway. Availability and Implementation: TimeTP is implemented in Python and available at http://biohealth.snu.ac.kr/software/TimeTP/. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: sunkim.bioinfo@snu.ac.kr PMID:27307609

Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma

PubMed Central

Gao, She-Gan; Liu, Rui-Min; Zhao, Yun-Gang; Wang, Pei; Ward, Douglas G.; Wang, Guang-Chao; Guo, Xiang-Qian; Gu, Juan; Niu, Wan-Bin; Zhang, Tian; Martin, Ashley; Guo, Zhi-Peng; Feng, Xiao-Shan; Qi, Yi-Jun; Ma, Yuan-Fang

2016-01-01

Combining MS-based proteomic data with network and topological features of such network would identify more clinically relevant molecules and meaningfully expand the repertoire of proteins derived from MS analysis. The integrative topological indexes representing 95.96% information of seven individual topological measures of node proteins were calculated within a protein-protein interaction (PPI) network, built using 244 differentially expressed proteins (DEPs) identified by iTRAQ 2D-LC-MS/MS. Compared with DEPs, differentially expressed genes (DEGs) and comprehensive features (CFs), structurally dominant nodes (SDNs) based on integrative topological index distribution produced comparable classification performance in three different clinical settings using five independent gene expression data sets. The signature molecules of SDN-based classifier for distinction of early from late clinical TNM stages were enriched in biological traits of protein synthesis, intracellular localization and ribosome biogenesis, which suggests that ribosome biogenesis represents a promising therapeutic target for treating ESCC. In addition, ITGB1 expression selected exclusively by integrative topological measures correlated with clinical stages and prognosis, which was further validated with two independent cohorts of ESCC samples. Thus the integrative topological analysis of PPI networks proposed in this study provides an alternative approach to identify potential biomarkers and therapeutic targets from MS/MS data with functional insights in ESCC. PMID:26898710

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

PubMed Central

Hsiao, Tzu-Hung; Chiu, Yu-Chiao; Hsu, Pei-Yin; Lu, Tzu-Pin; Lai, Liang-Chuan; Tsai, Mong-Hsun; Huang, Tim H.-M.; Chuang, Eric Y.; Chen, Yidong

2016-01-01

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC. PMID:26972162

Identifying Impacts Using Adaptive Fiber Bragg Grating Demodulator for Structural Health Monitoring Applications

NASA Astrophysics Data System (ADS)

Kirikera, G. R.; Balogun, O.; Krishnaswamy, S.

2008-02-01

A network of Fiber-Bragg Grating (FBG) sensors is developed as part of a Structural Health Monitoring system to identify impact damage. The sensor signals are adaptively demodulated using two-wave mixing (TWM) technology. The signals from multiple FBG sensors are multiplexed into a single TWM demodulator. The FBG sensor network is mounted on a plate, and the structure is subjected to impacts generated by dropping small ball bearings. Impact locations are identified based on time frequency analysis.

Candidate change agent identification among men at risk for HIV infection

PubMed Central

Schneider, John A.; McFadden, Rachel B.; Laumann, Edward O.; Kumar, SG Prem; Gandham, Sabitha R.; Oruganti, Ganesh

2012-01-01

Despite limited HIV prevention potency, peer-based programs have become one of the most often used HIV prevention approaches internationally. These programs demonstrate a need for greater specificity in peer change agent (PCA) recruitment and social network evaluation. In the present three-phase study based in India (2009–2010), we first explored the nature of friendship among truck-drivers, a group of men at high risk for HIV infection, in order to develop a thorough understanding of the social forces that contribute to and maintain their personal networks. This was accomplished in the first two study phases, through a combination of focus group discussions (n=5 groups), in-depth qualitative interviews (n=20), and personal network analyses (n=25) of truck-drivers to define friendship and deepen our understanding of friendship across geographic spaces. Measures collected in phases I and II included friend typologies, discussion topics, social network influences, advice-giving, and risk reduction. Outcomes were assessed through an iterative process of qualitative textual analysis and social network analysis. The networks of truck-drivers were found to comprise three typologies: close friends, parking lot friends, and other friends. From these data, we developed an algorithmic approach to the identification of a candidate PCA within a high-risk man’s personal network. In stage III we piloted field-use of this approach to identify and recruit PCAs, and further evaluated their potential for intervention through preliminary analysis of the PCA’s own personal networks. An instrument was developed to translate what social network theory and analysis has taught us about egocentric network dynamics into a real-world methodology for identifying intervention-appropriate peers within an individual’s personal network. Our approach can be tailored to the specifications of any high-risk population, and may serve to enhance current peer-based HIV interventions. PMID:22762951

Predicting new drug indications from network analysis

NASA Astrophysics Data System (ADS)

Mohd Ali, Yousoff Effendy; Kwa, Kiam Heong; Ratnavelu, Kurunathan

This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these networks, drugs were ranked based on their centrality scores whereby 18 prominent drugs from the drug-side effect network and 15 prominent drugs from the drug-indication network were identified. Indications and side effects of prominent drugs were deduced from the profiles of their neighbors in the networks and compared to existing clinical studies while an optimum threshold of similarity among drugs was sought for. The threshold can then be utilized for predicting indications and side effects of all drugs. Similarities of drugs were measured by the extent to which they share phenotypic profiles and neighbors. To improve the likelihood of accurate predictions, only profiles such as side effects of common or very common frequencies were considered. In summary, our work is an attempt to offer an alternative approach to drug repositioning using centrality measures commonly used for analyzing social networks.

Social Network Analysis for Assessing College-Aged Adults' Health: A Systematic Review.

PubMed

Patterson, Megan S; Go Odson, Patricia

2018-04-13

Social network analysis (SNA) is a useful, emerging method for studying health. College students are especially prone to social influence when it comes to health. This review aimed to identify network variables related to college student health and determine how SNA was used in the literature. A systematic review of relevant literature was conducted in October 2015. Studies employing egocentric or whole network analysis to study college student health were included. We used Garrard's Matrix Method to extract data from reviewed articles (n = 15). Drinking, smoking, aggression, homesickness, and stress were predicted by network variables in the reviewed literature. Methodological inconsistencies concerning boundary specification, data collection, nomination limits, and statistical analyses were revealed across studies. Results show the consistent relationship between network variables and college health outcomes, justifying further use of SNA to research college health. Suggestions and considerations for future use of SNA are provided.

Network Analysis of Rodent Transcriptomes in Spaceflight

NASA Technical Reports Server (NTRS)

Ramachandran, Maya; Fogle, Homer; Costes, Sylvain

2017-01-01

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

Towards a Methodology for Validation of Centrality Measures in Complex Networks

PubMed Central

2014-01-01

Background Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important? Purpose The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets. Method We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes. Results Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes. PMID:24709999

LINEBACkER: Bio-inspired Data Reduction Toward Real Time Network Traffic Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teuton, Jeremy R.; Peterson, Elena S.; Nordwall, Douglas J.

Abstract—One essential component of resilient cyber applications is the ability to detect adversaries and protect systems with the same flexibility adversaries will use to achieve their goals. Current detection techniques do not enable this degree of flexibility because most existing applications are built using exact or regular-expression matching to libraries of rule sets. Further, network traffic defies traditional cyber security approaches that focus on limiting access based on the use of passwords and examination of lists of installed or downloaded programs. These approaches do not readily apply to network traffic occurring beyond the access control point, and when the datamore » in question are combined control and payload data of ever increasing speed and volume. Manual analysis of network traffic is not normally possible because of the magnitude of the data that is being exchanged and the length of time that this analysis takes. At the same time, using an exact matching scheme to identify malicious traffic in real time often fails because the lists against which such searches must operate grow too large. In this work, we introduce an alternative method for cyber network detection based on similarity-measuring algorithms for gene sequence analysis. These methods are ideal because they were designed to identify similar but nonidentical sequences. We demonstrate that our method is generally applicable to the problem of network traffic analysis by illustrating its use in two different areas both based on different attributes of network traffic. Our approach provides a logical framework for organizing large collections of network data, prioritizing traffic of interest to human analysts, and makes it possible to discover traffic signatures without the bias introduced by expert-directed signature generation. Pattern recognition on reduced representations of network traffic offers a fast, efficient, and more robust way to detect anomalies.« less

An Actor-Network Theory Analysis of Policy Innovation for Smoke-Free Places: Understanding Change in Complex Systems

PubMed Central

Borland, Ron; Coghill, Ken

2010-01-01

Complex, transnational issues like the tobacco epidemic are major challenges that defy analysis and management by conventional methods, as are other public health issues, such as those associated with global food distribution and climate change. We examined the evolution of indoor smoke-free regulations, a tobacco control policy innovation, and identified the key attributes of those jurisdictions that successfully pursued this innovation and those that to date have not. In doing so, we employed the actor-network theory, a comprehensive framework for the analysis of fundamental system change. Through our analysis, we identified approaches to help overcome some systemic barriers to the solution of the tobacco problem and comment on other complex transnational problems. PMID:20466949

An actor-network theory analysis of policy innovation for smoke-free places: understanding change in complex systems.

PubMed

Young, David; Borland, Ron; Coghill, Ken

2010-07-01

Complex, transnational issues like the tobacco epidemic are major challenges that defy analysis and management by conventional methods, as are other public health issues, such as those associated with global food distribution and climate change. We examined the evolution of indoor smoke-free regulations, a tobacco control policy innovation, and identified the key attributes of those jurisdictions that successfully pursued this innovation and those that to date have not. In doing so, we employed the actor-network theory, a comprehensive framework for the analysis of fundamental system change. Through our analysis, we identified approaches to help overcome some systemic barriers to the solution of the tobacco problem and comment on other complex transnational problems.

Centrality measures in temporal networks with time series analysis

NASA Astrophysics Data System (ADS)

Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Wang, Xiaojie; Yi, Dongyun

2017-05-01

The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.

Identifying Repetitive Institutional Review Board Stipulations by Natural Language Processing and Network Analysis.

PubMed

Kury, Fabrício S P; Cimino, James J

2015-01-01

The corrections ("stipulations") to a proposed research study protocol produced by an institutional review board (IRB) can often be repetitive across many studies; however, there is no standard set of stipulations that could be used, for example, by researchers wishing to anticipate and correct problems in their research proposals prior to submitting to an IRB. The objective of the research was to computationally identify the most repetitive types of stipulations generated in the course of IRB deliberations. The text of each stipulation was normalized using the natural language processing techniques. An undirected weighted network was constructed in which each stipulation was represented by a node, and each link, if present, had weight corresponding to the TF-IDF Cosine Similarity of the stipulations. Network analysis software was then used to identify clusters in the network representing similar stipulations. The final results were correlated with additional data to produce further insights about the IRB workflow. From a corpus of 18,582 stipulations we identified 31 types of repetitive stipulations. Those types accounted for 3,870 stipulations (20.8% of the corpus) produced for 697 (88.7%) of all protocols in 392 (also 88.7%) of all the CNS IRB meetings with stipulations entered in our data source. A notable peroportion of the corrections produced by the IRB can be considered highly repetitive. Our shareable method relied on a minimal manual analysis and provides an intuitive exploration with theoretically unbounded granularity. Finer granularity allowed for the insight that is anticipated to prevent the need for identifying the IRB panel expertise or any human supervision.

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Na, Seungjin; Payne, Samuel H.; Bandeira, Nuno

The spectral networks approach enables the detection of pairs of spectra from related peptides and thus allows for the propagation of annotations from identified peptides to unidentified spectra. Beyond allowing for unbiased discovery of unexpected post-translational modifications, spectral networks are also applicable to multi-species comparative proteomics or metaproteomics to identify numerous orthologous versions of a protein. We present algorithmic and statistical advances in spectral networks that have made it possible to rigorously assess the statistical significance of spectral pairs and accurately estimate the error rate of identifications via propagation. In the analysis of three related Cyanothece species, a model organismmore » for biohydrogen production, spectral networks identified peptides with highly divergent sequences with up to dozens of variants per peptide, including many novel peptides in species that lack a sequenced genome. Furthermore, spectral networks strongly suggested the presence of novel peptides even in genomically characterized species (i.e. missing from databases) in that a significant portion of unidentified multi-species networks included at least two polymorphic peptide variants.« less

Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

DTIC Science & Technology

1998-05-01

Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants

PubMed Central

Moreira-Filho, Carlos Alberto; Bando, Silvia Yumi; Bertonha, Fernanda Bernardi; Silva, Filipi Nascimento; da Fontoura Costa, Luciano; Ferreira, Leandro Rodrigues; Furlanetto, Glaucio; Chacur, Paulo; Zerbini, Maria Claudia Nogueira; Carneiro-Sampaio, Magda

2016-01-01

Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue - obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT) - and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the “canonical” way of thymus functioning. Conversely, DS networks represent a “non-canonical” way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes. PMID:26848775

Ideological Think Tanks in the States: An Inventory of Their Prevalence, Networks, and Higher Education Policy Activity

ERIC Educational Resources Information Center

Ness, Erik C.; Gándara, Denisa

2014-01-01

This study takes an inventory of a particular type of intermediary organization ascendant within the state-level higher education policy: ideological think tanks. Our inventory identifies 99 think tanks: 59 affiliated with the conservative State Policy Network and 40 with the Progressive States Network. The analysis shows that state-level…

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

PubMed

Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Network-analysis-guided synthesis of weisaconitine D and liljestrandinine

NASA Astrophysics Data System (ADS)

Marth, C. J.; Gallego, G. M.; Lee, J. C.; Lebold, T. P.; Kulyk, S.; Kou, K. G. M.; Qin, J.; Lilien, R.; Sarpong, R.

2015-12-01

General strategies for the chemical synthesis of organic compounds, especially of architecturally complex natural products, are not easily identified. Here we present a method to establish a strategy for such syntheses, which uses network analysis. This approach has led to the identification of a versatile synthetic intermediate that facilitated syntheses of the diterpenoid alkaloids weisaconitine D and liljestrandinine, and the core of gomandonine. We also developed a web-based graphing program that allows network analysis to be easily performed on molecules with complex frameworks. The diterpenoid alkaloids comprise some of the most architecturally complex and functional-group-dense secondary metabolites isolated. Consequently, they present a substantial challenge for chemical synthesis. The synthesis approach described here is a notable departure from other single-target-focused strategies adopted for the syntheses of related structures. Specifically, it affords not only the targeted natural products, but also intermediates and derivatives in the three subfamilies of diterpenoid alkaloids (C-18, C-19 and C-20), and so provides a unified synthetic strategy for these natural products. This work validates the utility of network analysis as a starting point for identifying strategies for the syntheses of architecturally complex secondary metabolites.

Network analysis among HIV-infected young black men who have sex with men demonstrates high connectedness around few venues.

PubMed

Oster, Alexandra M; Wejnert, Cyprian; Mena, Leandro A; Elmore, Kim; Fisher, Holly; Heffelfinger, James D

2013-03-01

Network analysis is useful for understanding sexual transmission of HIV and other sexually transmitted infections. We conducted egocentric and affiliation network analysis among HIV-infected young black men who have sex with men (MSM) in the Jackson, Mississippi, area to understand networks and connectedness of this population. We interviewed 22 black MSM aged 17 to 25 years diagnosed as having HIV in 2006 to 2008. Participants provided demographic and geographic information about each sex partner during the 12 months before diagnosis and identified venues where they met these partners. We created affiliation network diagrams to understand connectedness of this population and identify venues that linked participants. The median number of partners reported was 4 (range, 1-16); a total of 97 partners (88 of whom were male) were reported. All but 1 participant were connected through a network of venues where they had met partners during the 12 months before diagnosis. Three venues were named as places for meeting partners by 13 of 22 participants. Participants reported having partners from all regions of Mississippi and 5 other states. HIV-infected young black MSM in this analysis were linked by a small number of venues. These venues should be targeted for testing and prevention interventions. The pattern of meeting sex partners in a small number of venues suggests densely connected networks that propagate infection. This pattern, in combination with sexual partnerships with persons from outside Jackson, may contribute to spread of HIV and other sexually transmitted infections into or out the Jackson area.

Flow Analysis Tool White Paper

NASA Technical Reports Server (NTRS)

Boscia, Nichole K.

2012-01-01

Faster networks are continually being built to accommodate larger data transfers. While it is intuitive to think that implementing faster networks will result in higher throughput rates, this is often not the case. There are many elements involved in data transfer, many of which are beyond the scope of the network itself. Although networks may get bigger and support faster technologies, the presence of other legacy components, such as older application software or kernel parameters, can often cause bottlenecks. Engineers must be able to identify when data flows are reaching a bottleneck that is not imposed by the network and then troubleshoot it using the tools available to them. The current best practice is to collect as much information as possible on the network traffic flows so that analysis is quick and easy. Unfortunately, no single method of collecting this information can sufficiently capture the whole endto- end picture. This becomes even more of a hurdle when large, multi-user systems are involved. In order to capture all the necessary information, multiple data sources are required. This paper presents a method for developing a flow analysis tool to effectively collect network flow data from multiple sources and provide that information to engineers in a clear, concise way for analysis. The purpose of this method is to collect enough information to quickly (and automatically) identify poorly performing flows along with the cause of the problem. The method involves the development of a set of database tables that can be populated with flow data from multiple sources, along with an easyto- use, web-based front-end interface to help network engineers access, organize, analyze, and manage all the information.

Network Structure and Travel Time Perception

PubMed Central

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time. PMID:24204932

Resting State Network Topology of the Ferret Brain

PubMed Central

Zhou, Zhe Charles; Salzwedel, Andrew P.; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K.; Gilmore, John H.; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-01-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 tesla MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. PMID:27596024

Network analysis applications in hydrology

NASA Astrophysics Data System (ADS)

Price, Katie

2017-04-01

Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

WGCNA: an R package for weighted correlation network analysis.

PubMed

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

An option for measuring maternal mortality in developing countries: a survey using community informants.

PubMed

Qomariyah, Siti Nurul; Braunholtz, David; Achadi, Endang L; Witten, Karen H; Pambudi, Eko Setyo; Anggondowati, Trisari; Latief, Kamaluddin; Graham, Wendy J

2010-11-17

The maternal mortality ratio (MMR) remains high in most developing countries. Local, recent estimates of MMR are needed to motivate policymakers and evaluate interventions. But, estimating MMR, in the absence of vital registration systems, is difficult. This paper describes an efficient approach using village informant networks to capture maternal death cases (Maternal Deaths from Informants/Maternal Death Follow on Review or MADE-IN/MADE-FOR) developed to address this gap, and examines its validity and efficiency. MADE-IN used two village informant networks - heads of neighbourhood units (RTs) and health volunteers (Kaders). Informants were invited to attend separate network meetings - through the village head (for the RT) and through health centre for the kaders. Attached to the letter was a form with written instructions requesting informants list deaths of women of reproductive age (WRA) in the village during the previous two years. At a 'listing meeting' the informants' understanding on the form was checked, informants could correct their forms, and then collectively agreed a consolidated list. MADE-FOR consisted of visits relatives of likely pregnancy related deaths (PRDs) identified from MADE-IN, to confirm the PRD status and gather information about the cause of death. Capture-recapture (CRC) analysis enabled estimation of coverage rates of the two networks, and of total PRDs. The RT network identified a higher proportion of PRDs than the kaders (estimated 0.85 vs. 0.71), but the latter was easier and cheaper to access. Assigned PRD status amongst identified WRA deaths was more accurate for the kader network, and seemingly for more recent deaths, and for deaths from rural areas. Assuming information on live births from an existing source to calculate the MMR, MADE-IN/MADE-FOR cost only $0.1 (US) per women-year risk of exposure, substantially cheaper than alternatives. This study shows that reliable local, recent estimates of MMR can be obtained relatively cheaply using two independent informant networks to identify cases. Neither network captured all PRDs, but capture-recapture analysis allowed self-calibration. However, it requires careful avoidance of false-positives, and matching of cases identified by both networks, which was achieved by the home visit.

Identifying compromised systems through correlation of suspicious traffic from malware behavioral analysis

NASA Astrophysics Data System (ADS)

Camilo, Ana E. F.; Grégio, André; Santos, Rafael D. C.

2016-05-01

Malware detection may be accomplished through the analysis of their infection behavior. To do so, dynamic analysis systems run malware samples and extract their operating system activities and network traffic. This traffic may represent malware accessing external systems, either to steal sensitive data from victims or to fetch other malicious artifacts (configuration files, additional modules, commands). In this work, we propose the use of visualization as a tool to identify compromised systems based on correlating malware communications in the form of graphs and finding isomorphisms between them. We produced graphs from over 6 thousand distinct network traffic files captured during malware execution and analyzed the existing relationships among malware samples and IP addresses.

Visualizando el desarrollo de la nanomedicina en México.

PubMed

Robles-Belmont, Eduardo; Gortari-Rabiela, Rebeca de; Galarza-Barrios, Pilar; Siqueiros-García, Jesús Mario; Ruiz-León, Alejandro Arnulfo

2017-01-01

In this article we present a set of different visualizations of Mexico's nanomedicine scientific production data. Visualizations were developed using different methodologies for data analysis and visualization such as social network analysis, geography of science maps, and complex network communities analysis. Results are a multi-dimensional overview of the evolution of nanomedicine in Mexico. Moreover, visualizations allowed to identify trends and patterns of collaboration at the national and international level. Trends are also found in the knowledge structure of themes and disciplines. Finally, we identified the scientific communities in Mexico that are responsible for the new knowledge production in this emergent field of science. Copyright: © 2017 SecretarÍa de Salud

A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

PubMed

Jambusaria, Ankit; Klomp, Jeff; Hong, Zhigang; Rafii, Shahin; Dai, Yang; Malik, Asrar B; Rehman, Jalees

2018-06-07

The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues. Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database. HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes.

Analysis of Near-field of Circular Aperture Antennas with Application to Study of High Intensity Radio Frequency (HIRF) Hazards to Aviation from JPL/NASA Deep Space Network Antennas

NASA Technical Reports Server (NTRS)

Jamnejad, Vahraz; Statman, Joseph

2013-01-01

This work includes a simplified analysis of the radiated near to mid-field from JPL/NASA Deep Space Network (DSN) reflector antennas and uses an averaging technique over the main beam region and beyond for complying with FAA regulations in specific aviation environments. The work identifies areas that require special attention, including the implications of the very narrow beam of the DSN transmitters. The paper derives the maximum averaged power densities allowed and identifies zones where mitigation measures are required.

Data Mining of Network Logs

NASA Technical Reports Server (NTRS)

Collazo, Carlimar

2011-01-01

The statement of purpose is to analyze network monitoring logs to support the computer incident response team. Specifically, gain a clear understanding of the Uniform Resource Locator (URL) and its structure, and provide a way to breakdown a URL based on protocol, host name domain name, path, and other attributes. Finally, provide a method to perform data reduction by identifying the different types of advertisements shown on a webpage for incident data analysis. The procedures used for analysis and data reduction will be a computer program which would analyze the URL and identify and advertisement links from the actual content links.

Network analysis shining light on parasite ecology and diversity.

PubMed

Poulin, Robert

2010-10-01

The vast number of species making up natural communities, and the myriad interactions among them, pose great difficulties for the study of community structure, dynamics and stability. Borrowed from other fields, network analysis is making great inroads in community ecology and is only now being applied to host-parasite interactions. It allows a complex system to be examined in its entirety, as opposed to one or a few components at a time. This review explores what network analysis is and how it can be used to investigate parasite ecology. It also summarizes the first findings to emerge from network analyses of host-parasite interactions and identifies promising future directions made possible by this approach. Copyright © 2010 Elsevier Ltd. All rights reserved.

Network Analytical Tool for Monitoring Global Food Safety Highlights China

PubMed Central

Nepusz, Tamás; Petróczi, Andrea; Naughton, Declan P.

2009-01-01

Background The Beijing Declaration on food safety and security was signed by over fifty countries with the aim of developing comprehensive programs for monitoring food safety and security on behalf of their citizens. Currently, comprehensive systems for food safety and security are absent in many countries, and the systems that are in place have been developed on different principles allowing poor opportunities for integration. Methodology/Principal Findings We have developed a user-friendly analytical tool based on network approaches for instant customized analysis of food alert patterns in the European dataset from the Rapid Alert System for Food and Feed. Data taken from alert logs between January 2003 – August 2008 were processed using network analysis to i) capture complexity, ii) analyze trends, and iii) predict possible effects of interventions by identifying patterns of reporting activities between countries. The detector and transgressor relationships are readily identifiable between countries which are ranked using i) Google's PageRank algorithm and ii) the HITS algorithm of Kleinberg. The program identifies Iran, China and Turkey as the transgressors with the largest number of alerts. However, when characterized by impact, counting the transgressor index and the number of countries involved, China predominates as a transgressor country. Conclusions/Significance This study reports the first development of a network analysis approach to inform countries on their transgressor and detector profiles as a user-friendly aid for the adoption of the Beijing Declaration. The ability to instantly access the country-specific components of the several thousand annual reports will enable each country to identify the major transgressors and detectors within its trading network. Moreover, the tool can be used to monitor trading countries for improved detector/transgressor ratios. PMID:19688088

Elementary Mode Analysis: A Useful Metabolic Pathway Analysis Tool for Characterizing Cellular Metabolism

PubMed Central

Trinh, Cong T.; Wlaschin, Aaron; Srienc, Friedrich

2010-01-01

Elementary Mode Analysis is a useful Metabolic Pathway Analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering. PMID:19015845

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abercrombie, Robert K; Udoeyop, Akaninyene W; Schlicher, Bob G

This work examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, patents, news archives, andmore » online mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain we investigated.« less

Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ba, Qian; Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing; Li, Junyang

2015-03-01

Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong tomore » the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.« less

In-Silico Identification Of Micro-Loops In Myelodysplastic Syndromes

NASA Astrophysics Data System (ADS)

Beck, Dominik; Brandl, Miriam; Pham, Tuan D.; Chang, Chung-Che; Zhou, Xiaobo

2011-06-01

Micro-loops are regulatory network motifs that leverage transcriptional and posttranscriptional control to effectively regulate the transcriptome. In this paper a regulatory network for Myelodysplastic Syndromes (MDSs) was constructed from the literature and publicly available data sources. The network was filtered using data from deep-sequencing of small RNAs, exon and microarrays. Motif discovery showed that micro-loops might exist in MDS. We further used the identified micro-loops and performed basic network analysis to identify the known disease gene RUNX1/AML, as well as miRNA family hsa-mir-181. This suggested that the concept of micro-loops can be applied to enhance disease gene identification and biomarker discovery.

Compressive Network Analysis

PubMed Central

Jiang, Xiaoye; Yao, Yuan; Liu, Han; Guibas, Leonidas

2014-01-01

Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets. PMID:25620806

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model.

PubMed

Mazaya, Maulida; Trinh, Hung-Cuong; Kwon, Yung-Keun

2017-12-21

Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships. Taken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.

A user exposure based approach for non-structural road network vulnerability analysis

PubMed Central

Jin, Lei; Wang, Haizhong; Yu, Le; Liu, Lin

2017-01-01

Aiming at the dense urban road network vulnerability without structural negative consequences, this paper proposes a novel non-structural road network vulnerability analysis framework. Three aspects of the framework are mainly described: (i) the rationality of non-structural road network vulnerability, (ii) the metrics for negative consequences accounting for variant road conditions, and (iii) the introduction of a new vulnerability index based on user exposure. Based on the proposed methodology, a case study in the Sioux Falls network which was usually threatened by regular heavy snow during wintertime is detailedly discussed. The vulnerability ranking of links of Sioux Falls network with respect to heavy snow scenario is identified. As a result of non-structural consequences accompanied by conceivable degeneration of network, there are significant increases in generalized travel time costs which are measurements for “emotionally hurt” of topological road network. PMID:29176832

An investigation of the impact of using different methods for network meta-analysis: a protocol for an empirical evaluation.

PubMed

Karahalios, Amalia Emily; Salanti, Georgia; Turner, Simon L; Herbison, G Peter; White, Ian R; Veroniki, Areti Angeliki; Nikolakopoulou, Adriani; Mckenzie, Joanne E

2017-06-24

Network meta-analysis, a method to synthesise evidence from multiple treatments, has increased in popularity in the past decade. Two broad approaches are available to synthesise data across networks, namely, arm- and contrast-synthesis models, with a range of models that can be fitted within each. There has been recent debate about the validity of the arm-synthesis models, but to date, there has been limited empirical evaluation comparing results using the methods applied to a large number of networks. We aim to address this gap through the re-analysis of a large cohort of published networks of interventions using a range of network meta-analysis methods. We will include a subset of networks from a database of network meta-analyses of randomised trials that have been identified and curated from the published literature. The subset of networks will include those where the primary outcome is binary, the number of events and participants are reported for each direct comparison, and there is no evidence of inconsistency in the network. We will re-analyse the networks using three contrast-synthesis methods and two arm-synthesis methods. We will compare the estimated treatment effects, their standard errors, treatment hierarchy based on the surface under the cumulative ranking (SUCRA) curve, the SUCRA value, and the between-trial heterogeneity variance across the network meta-analysis methods. We will investigate whether differences in the results are affected by network characteristics and baseline risk. The results of this study will inform whether, in practice, the choice of network meta-analysis method matters, and if it does, in what situations differences in the results between methods might arise. The results from this research might also inform future simulation studies.

Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes.

PubMed

Feng, Yinling; Wang, Xuefeng

2017-03-01

In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co‑expression networks and clinical information was adopted, using weighted gene co‑expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co‑pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution‑based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD‑associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus

PubMed Central

Pathania, Shivalika; Bagler, Ganesh; Ahuja, Paramvir S.

2016-01-01

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites. PMID:27588023

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus.

PubMed

Pathania, Shivalika; Bagler, Ganesh; Ahuja, Paramvir S

2016-01-01

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites.

Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan.

PubMed

Davison, Elizabeth N; Turner, Benjamin O; Schlesinger, Kimberly J; Miller, Michael B; Grafton, Scott T; Bassett, Danielle S; Carlson, Jean M

2016-11-01

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory"), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.

How to Identify Success Among Networks That Promote Active Living.

PubMed

Litt, Jill; Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-11-01

We evaluated organization- and network-level factors that influence organizations' perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. A total of 53 of 59 "whole networks" met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%-100%). Occupying a leadership position (P < .01), the amount of time with the network (P < .05), and support from community leaders (P < .05) emerged as correlates of perceived success. Organizations' perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities.

Surrogate-assisted identification of influences of network construction on evolving weighted functional networks

NASA Astrophysics Data System (ADS)

Stahn, Kirsten; Lehnertz, Klaus

2017-12-01

We aim at identifying factors that may affect the characteristics of evolving weighted networks derived from empirical observations. To this end, we employ various chains of analysis that are often used in field studies for a data-driven derivation and characterization of such networks. As an example, we consider fully connected, weighted functional brain networks before, during, and after epileptic seizures that we derive from multichannel electroencephalographic data recorded from epilepsy patients. For these evolving networks, we estimate clustering coefficient and average shortest path length in a time-resolved manner. Lastly, we make use of surrogate concepts that we apply at various levels of the chain of analysis to assess to what extent network characteristics are dominated by properties of the electroencephalographic recordings and/or the evolving weighted networks, which may be accessible more easily. We observe that characteristics are differently affected by the unavoidable referencing of the electroencephalographic recording, by the time-series-analysis technique used to derive the properties of network links, and whether or not networks were normalized. Importantly, for the majority of analysis settings, we observe temporal evolutions of network characteristics to merely reflect the temporal evolutions of mean interaction strengths. Such a property of the data may be accessible more easily, which would render the weighted network approach—as used here—as an overly complicated description of simple aspects of the data.

Reduction of Complexity: An Aspect of Network Visualization

DTIC Science & Technology

2006-12-01

research is to identify strategies for the visualization of network information. Distinction can be made between visual communication and visual...exploration (MacEachern 1994). Visual communication deals with how to visualize results of different kinds of analysis, i.e., visualization in the case

Fyn-Dependent Gene Networks in Acute Ethanol Sensitivity

PubMed Central

Farris, Sean P.; Miles, Michael F.

2013-01-01

Studies in humans and animal models document that acute behavioral responses to ethanol are predisposing factor for the risk of long-term drinking behavior. Prior microarray data from our laboratory document strain- and brain region-specific variation in gene expression profile responses to acute ethanol that may be underlying regulators of ethanol behavioral phenotypes. The non-receptor tyrosine kinase Fyn has previously been mechanistically implicated in the sedative-hypnotic response to acute ethanol. To further understand how Fyn may modulate ethanol behaviors, we used whole-genome expression profiling. We characterized basal and acute ethanol-evoked (3 g/kg) gene expression patterns in nucleus accumbens (NAC), prefrontal cortex (PFC), and ventral midbrain (VMB) of control and Fyn knockout mice. Bioinformatics analysis identified a set of Fyn-related gene networks differently regulated by acute ethanol across the three brain regions. In particular, our analysis suggested a coordinate basal decrease in myelin-associated gene expression within NAC and PFC as an underlying factor in sensitivity of Fyn null animals to ethanol sedation. An in silico analysis across the BXD recombinant inbred (RI) strains of mice identified a significant correlation between Fyn expression and a previously published ethanol loss-of-righting-reflex (LORR) phenotype. By combining PFC gene expression correlates to Fyn and LORR across multiple genomic datasets, we identified robust Fyn-centric gene networks related to LORR. Our results thus suggest that multiple system-wide changes exist within specific brain regions of Fyn knockout mice, and that distinct Fyn-dependent expression networks within PFC may be important determinates of the LORR due to acute ethanol. These results add to the interpretation of acute ethanol behavioral sensitivity in Fyn kinase null animals, and identify Fyn-centric gene networks influencing variance in ethanol LORR. Such networks may also inform future design of pharmacotherapies for the treatment and prevention of alcohol use disorders. PMID:24312422

WGCNA: an R package for weighted correlation network analysis

PubMed Central

Langfelder, Peter; Horvath, Steve

2008-01-01

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at . PMID:19114008

Multiscale complex network analysis: An approach to study spatiotemporal rainfall pattern in south Germany

NASA Astrophysics Data System (ADS)

Agarwal, Ankit; Marwan, Norbert; Rathinasamy, Maheswaran; Oeztuerk, Ugur; Merz, Bruno; Kurths, Jürgen

2017-04-01

Understanding of the climate sytems has been of tremendous importance to different branches such as agriculture, flood, drought and water resources management etc. In this regard, complex networks analysis and time series analysis attracted considerable attention, owing to their potential role in understanding the climate system through characteristic properties. One of the basic requirements in studying climate network dynamics is to identify connections in space or time or space-time, depending upon the purpose. Although a wide variety of approaches have been developed and applied to identify and analyse spatio-temporal relationships by climate networks, there is still further need for improvements in particular when considering precipitation time series or interactions on different scales. In this regard, recent developments in the area of network theory, especially complex networks, offer new avenues, both for their generality about systems and for their holistic perspective about spatio-temporal relationships. The present study has made an attempt to apply the ideas developed in the field of complex networks to examine connections in regional climate networks with particular focus on multiscale spatiotemporal connections. This paper proposes a novel multiscale understanding of regional climate networks using wavelets. The proposed approach is applied to daily precipitation records observed at 543 selected stations from south Germany for a period of 110 years (1901-2010). Further, multiscale community mining is performed on the same study region to shed more light on the underlying processes at different time scales. Various network measure and tools so far employed provide micro-level (individual station) and macro-level (community structure) information of the network. It is interesting to investigate how the result of this study can be useful for future climate predictions and for evaluating climate models on their implementation regarding heavy precipitation. Keywords: Complex network, event synchronization, wavelet, regional climate network, multiscale community mining

Network analysis of oyster transcriptome revealed a cascade of cellular responses during recovery after heat shock.

PubMed

Zhang, Lingling; Hou, Rui; Su, Hailin; Hu, Xiaoli; Wang, Shi; Bao, Zhenmin

2012-01-01

Oysters, as a major group of marine bivalves, can tolerate a wide range of natural and anthropogenic stressors including heat stress. Recent studies have shown that oysters pretreated with heat shock can result in induced heat tolerance. A systematic study of cellular recovery from heat shock may provide insights into the mechanism of acquired thermal tolerance. In this study, we performed the first network analysis of oyster transcriptome by reanalyzing microarray data from a previous study. Network analysis revealed a cascade of cellular responses during oyster recovery after heat shock and identified responsive gene modules and key genes. Our study demonstrates the power of network analysis in a non-model organism with poor gene annotations, which can lead to new discoveries that go beyond the focus on individual genes.

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks*

PubMed Central

Bandeira, Nuno

2016-01-01

Peptide and protein identification remains challenging in organisms with poorly annotated or rapidly evolving genomes, as are commonly encountered in environmental or biofuels research. Such limitations render tandem mass spectrometry (MS/MS) database search algorithms ineffective as they lack corresponding sequences required for peptide-spectrum matching. We address this challenge with the spectral networks approach to (1) match spectra of orthologous peptides across multiple related species and then (2) propagate peptide annotations from identified to unidentified spectra. We here present algorithms to assess the statistical significance of spectral alignments (Align-GF), reduce the impurity in spectral networks, and accurately estimate the error rate in propagated identifications. Analyzing three related Cyanothece species, a model organism for biohydrogen production, spectral networks identified peptides from highly divergent sequences from networks with dozens of variant peptides, including thousands of peptides in species lacking a sequenced genome. Our analysis further detected the presence of many novel putative peptides even in genomically characterized species, thus suggesting the possibility of gaps in our understanding of their proteomic and genomic expression. A web-based pipeline for spectral networks analysis is available at http://proteomics.ucsd.edu/software. PMID:27609420

Systems Genetic Analyses Highlight a TGFβ-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease.

PubMed

Scarpa, Joseph R; Jiang, Peng; Losic, Bojan; Readhead, Ben; Gao, Vance D; Dudley, Joel T; Vitaterna, Martha H; Turek, Fred W; Kasarskis, Andrew

2016-07-01

Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.

PubMed

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C M

2016-03-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The "competition" (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest--ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. Copyright © 2015 Elsevier Ltd. All rights reserved.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning

PubMed Central

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C.M.

2016-01-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The “competition” (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest – ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. PMID:26866283

In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma

PubMed Central

Zinati, Zahra; Shamloo-Dashtpagerdi, Roohollah; Behpouri, Ali

2016-01-01

As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites. PMID:28261627

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

PubMed Central

Kaushik, Abhinav; Ali, Shakir; Gupta, Dinesh

2017-01-01

Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA. PMID:28084397

Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.

PubMed

Muetze, Tanja; Goenawan, Ivan H; Wiencko, Heather L; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J

2016-01-01

Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).

Metabolic Profiling of a Mapping Population Exposes New Insights in the Regulation of Seed Metabolism and Seed, Fruit, and Plant Relations

PubMed Central

Toubiana, David; Semel, Yaniv; Tohge, Takayuki; Beleggia, Romina; Cattivelli, Luigi; Rosental, Leah; Nikoloski, Zoran; Zamir, Dani; Fernie, Alisdair R.; Fait, Aaron

2012-01-01

To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping. PMID:22479206

Particle identification with neural networks using a rotational invariant moment representation

NASA Astrophysics Data System (ADS)

Sinkus, R.; Voss, T.

1997-02-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The multidimensional input distribution for the neural network is transformed via a principle component analysis and rescaled by its respective variances to ensure input values of the order of one. This results is a better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies.

Novel presentational approaches were developed for reporting network meta-analysis.

PubMed

Tan, Sze Huey; Cooper, Nicola J; Bujkiewicz, Sylwia; Welton, Nicky J; Caldwell, Deborah M; Sutton, Alexander J

2014-06-01

To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses. The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles. Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify "top-ranking" interventions. The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician. Copyright © 2014 Elsevier Inc. All rights reserved.

Heading in the right direction: thermodynamics-based network analysis and pathway engineering.

PubMed

Ataman, Meric; Hatzimanikatis, Vassily

2015-12-01

Thermodynamics-based network analysis through the introduction of thermodynamic constraints in metabolic models allows a deeper analysis of metabolism and guides pathway engineering. The number and the areas of applications of thermodynamics-based network analysis methods have been increasing in the last ten years. We review recent applications of these methods and we identify the areas that such analysis can contribute significantly, and the needs for future developments. We find that organisms with multiple compartments and extremophiles present challenges for modeling and thermodynamics-based flux analysis. The evolution of current and new methods must also address the issues of the multiple alternatives in flux directionalities and the uncertainties and partial information from analytical methods. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

PubMed Central

Vinayagam, Arunachalam; Gibson, Travis E.; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

2016-01-01

The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets. PMID:27091990

Robustness and Recovery of Lifeline Infrastructure and Ecosystem Networks

NASA Astrophysics Data System (ADS)

Bhatia, U.; Ganguly, A. R.

2015-12-01

Disruptive events, both natural and man-made, can have widespread impacts on both natural systems and lifeline infrastructure networks leading to the loss of biodiversity and essential functionality, respectively. Projected sea-level rise and climate change can further increase the frequency and severity of large-scale floods on urban-coastal megacities. Nevertheless, Failure in infrastructure systems can trigger cascading impacts on dependent ecosystems, and vice-versa. An important consideration in the behavior of the isolated networks and inter-connected networks following disruptive events is their resilience, or the ability of the network to "bounce back" to a pre-disaster state. Conventional risk analysis and subsequent risk management frameworks have focused on identifying the components' vulnerability and strengthening of the isolated components to withstand these disruptions. But high interconnectedness of these systems, and evolving nature of hazards, particularly in the context of climate extremes, make the component level analysis unrealistic. In this study, we discuss the complex network-based resilience framework to understand fragility and recovery strategies for infrastructure systems impacted by climate-related hazards. We extend the proposed framework to assess the response of ecological networks to multiple species loss and design the restoration management framework to identify the most efficient restoration sequence of species, which can potentially lead to disproportionate gains in biodiversity.

Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

DOE PAGES

Pavlopoulos, Georgios A.; Paez-Espino, David; Kyrpides, Nikos C.; ...

2017-07-18

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. Lastly, we comment on their strengths and their weaknesses andmore » empirically discuss their scalability, user friendliness, and postvisualization capabilities.« less

Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pavlopoulos, Georgios A.; Paez-Espino, David; Kyrpides, Nikos C.

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. Lastly, we comment on their strengths and their weaknesses andmore » empirically discuss their scalability, user friendliness, and postvisualization capabilities.« less

Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations.

PubMed

Demertzi, Athena; Gómez, Francisco; Crone, Julia Sophia; Vanhaudenhuyse, Audrey; Tshibanda, Luaba; Noirhomme, Quentin; Thonnard, Marie; Charland-Verville, Vanessa; Kirsch, Murielle; Laureys, Steven; Soddu, Andrea

2014-03-01

In healthy conditions, group-level fMRI resting state analyses identify ten resting state networks (RSNs) of cognitive relevance. Here, we aim to assess the ten-network model in severely brain-injured patients suffering from disorders of consciousness and to identify those networks which will be most relevant to discriminate between patients and healthy subjects. 300 fMRI volumes were obtained in 27 healthy controls and 53 patients in minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS) and coma. Independent component analysis (ICA) reduced data dimensionality. The ten networks were identified by means of a multiple template-matching procedure and were tested on neuronality properties (neuronal vs non-neuronal) in a data-driven way. Univariate analyses detected between-group differences in networks' neuronal properties and estimated voxel-wise functional connectivity in the networks, which were significantly less identifiable in patients. A nearest-neighbor "clinical" classifier was used to determine the networks with high between-group discriminative accuracy. Healthy controls were characterized by more neuronal components compared to patients in VS/UWS and in coma. Compared to healthy controls, fewer patients in MCS and VS/UWS showed components of neuronal origin for the left executive control network, default mode network (DMN), auditory, and right executive control network. The "clinical" classifier indicated the DMN and auditory network with the highest accuracy (85.3%) in discriminating patients from healthy subjects. FMRI multiple-network resting state connectivity is disrupted in severely brain-injured patients suffering from disorders of consciousness. When performing ICA, multiple-network testing and control for neuronal properties of the identified RSNs can advance fMRI system-level characterization. Automatic data-driven patient classification is the first step towards future single-subject objective diagnostics based on fMRI resting state acquisitions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

PubMed Central

Creixell, Pau; Schoof, Erwin M.; Simpson, Craig D.; Longden, James; Miller, Chad J.; Lou, Hua Jane; Perryman, Lara; Cox, Thomas R.; Zivanovic, Nevena; Palmeri, Antonio; Wesolowska-Andersen, Agata; Helmer-Citterich, Manuela; Ferkinghoff-Borg, Jesper; Itamochi, Hiroaki; Bodenmiller, Bernd; Erler, Janine T.; Turk, Benjamin E.; Linding, Rune

2015-01-01

Summary Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks. PMID:26388441

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

PubMed

Schmidt, Helmut; Petkov, George; Richardson, Mark P; Terry, John R

2014-11-01

Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.

Dynamic facial expressions evoke distinct activation in the face perception network: a connectivity analysis study.

PubMed

Foley, Elaine; Rippon, Gina; Thai, Ngoc Jade; Longe, Olivia; Senior, Carl

2012-02-01

Very little is known about the neural structures involved in the perception of realistic dynamic facial expressions. In the present study, a unique set of naturalistic dynamic facial emotional expressions was created. Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend Haxby et al.'s [Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. The distributed human neural system for face perception. Trends in Cognitive Science, 4, 223-233, 2000] distributed neural system for face perception. This network includes early visual regions, such as the inferior occipital gyrus, which is identified as insensitive to motion or affect but sensitive to the visual stimulus, the STS, identified as specifically sensitive to motion, and the amygdala, recruited to process affect. Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as the inferior occipital gyrus and the STS, along with coupling between the STS and the amygdala, as well as the inferior frontal gyrus. These findings support the presence of a distributed network of cortical regions that mediate the perception of different dynamic facial expressions.

Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes.

PubMed

Fan, Wufeng; Zhou, Yuhan; Li, Hao

2017-01-01

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.

A microRNA-mRNA expression network during oral siphon regeneration in Ciona.

PubMed

Spina, Elijah J; Guzman, Elmer; Zhou, Hongjun; Kosik, Kenneth S; Smith, William C

2017-05-15

Here we present a parallel study of mRNA and microRNA expression during oral siphon (OS) regeneration in Ciona robusta , and the derived network of their interactions. In the process of identifying 248 mRNAs and 15 microRNAs as differentially expressed, we also identified 57 novel microRNAs, several of which are among the most highly differentially expressed. Analysis of functional categories identified enriched transcripts related to stress responses and apoptosis at the wound healing stage, signaling pathways including Wnt and TGFβ during early regrowth, and negative regulation of extracellular proteases in late stage regeneration. Consistent with the expression results, we found that inhibition of TGFβ signaling blocked OS regeneration. A correlation network was subsequently inferred for all predicted microRNA-mRNA target pairs expressed during regeneration. Network-based clustering associated transcripts into 22 non-overlapping groups, the functional analysis of which showed enrichment of stress response, signaling pathway and extracellular protease categories that could be related to specific microRNAs. Predicted targets of the miR-9 cluster suggest a role in regulating differentiation and the proliferative state of neural progenitors through regulation of the cytoskeleton and cell cycle. © 2017. Published by The Company of Biologists Ltd.

A microRNA-mRNA expression network during oral siphon regeneration in Ciona

PubMed Central

Spina, Elijah J.; Guzman, Elmer; Zhou, Hongjun; Kosik, Kenneth S.

2017-01-01

Here we present a parallel study of mRNA and microRNA expression during oral siphon (OS) regeneration in Ciona robusta, and the derived network of their interactions. In the process of identifying 248 mRNAs and 15 microRNAs as differentially expressed, we also identified 57 novel microRNAs, several of which are among the most highly differentially expressed. Analysis of functional categories identified enriched transcripts related to stress responses and apoptosis at the wound healing stage, signaling pathways including Wnt and TGFβ during early regrowth, and negative regulation of extracellular proteases in late stage regeneration. Consistent with the expression results, we found that inhibition of TGFβ signaling blocked OS regeneration. A correlation network was subsequently inferred for all predicted microRNA-mRNA target pairs expressed during regeneration. Network-based clustering associated transcripts into 22 non-overlapping groups, the functional analysis of which showed enrichment of stress response, signaling pathway and extracellular protease categories that could be related to specific microRNAs. Predicted targets of the miR-9 cluster suggest a role in regulating differentiation and the proliferative state of neural progenitors through regulation of the cytoskeleton and cell cycle. PMID:28432214

TimeXNet Web: Identifying cellular response networks from diverse omics time-course data.

PubMed

Tan, Phit Ling; López, Yosvany; Nakai, Kenta; Patil, Ashwini

2018-05-14

Condition-specific time-course omics profiles are frequently used to study cellular response to stimuli and identify associated signaling pathways. However, few online tools allow users to analyze multiple types of high-throughput time-course data. TimeXNet Web is a web server that extracts a time-dependent gene/protein response network from time-course transcriptomic, proteomic or phospho-proteomic data, and an input interaction network. It classifies the given genes/proteins into time-dependent groups based on the time of their highest activity and identifies the most probable paths connecting genes/proteins in consecutive groups. The response sub-network is enriched in activated genes/proteins and contains novel regulators that do not show any observable change in the input data. Users can view the resultant response network and analyze it for functional enrichment. TimeXNet Web supports the analysis of high-throughput data from multiple species by providing high quality, weighted protein-protein interaction networks for 12 model organisms. http://txnet.hgc.jp/. ashwini@hgc.jp. Supplementary data are available at Bioinformatics online.

Independent component analysis (ICA) and self-organizing map (SOM) approach to multidetection system for network intruders

NASA Astrophysics Data System (ADS)

Abdi, Abdi M.; Szu, Harold H.

2003-04-01

With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System (IDS) is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today"s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA. Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.

Structural analysis of health-relevant policy-making information exchange networks in Canada.

PubMed

Contandriopoulos, Damien; Benoît, François; Bryant-Lukosius, Denise; Carrier, Annie; Carter, Nancy; Deber, Raisa; Duhoux, Arnaud; Greenhalgh, Trisha; Larouche, Catherine; Leclerc, Bernard-Simon; Levy, Adrian; Martin-Misener, Ruth; Maximova, Katerina; McGrail, Kimberlyn; Nykiforuk, Candace; Roos, Noralou; Schwartz, Robert; Valente, Thomas W; Wong, Sabrina; Lindquist, Evert; Pullen, Carolyn; Lardeux, Anne; Perroux, Melanie

2017-09-20

Health systems worldwide struggle to identify, adopt, and implement in a timely and system-wide manner the best-evidence-informed-policy-level practices. Yet, there is still only limited evidence about individual and institutional best practices for fostering the use of scientific evidence in policy-making processes The present project is the first national-level attempt to (1) map and structurally analyze-quantitatively-health-relevant policy-making networks that connect evidence production, synthesis, interpretation, and use; (2) qualitatively investigate the interaction patterns of a subsample of actors with high centrality metrics within these networks to develop an in-depth understanding of evidence circulation processes; and (3) combine these findings in order to assess a policy network's "absorptive capacity" regarding scientific evidence and integrate them into a conceptually sound and empirically grounded framework. The project is divided into two research components. The first component is based on quantitative analysis of ties (relationships) that link nodes (participants) in a network. Network data will be collected through a multi-step snowball sampling strategy. Data will be analyzed structurally using social network mapping and analysis methods. The second component is based on qualitative interviews with a subsample of the Web survey participants having central, bridging, or atypical positions in the network. Interviews will focus on the process through which evidence circulates and enters practice. Results from both components will then be integrated through an assessment of the network's and subnetwork's effectiveness in identifying, capturing, interpreting, sharing, reframing, and recodifying scientific evidence in policy-making processes. Knowledge developed from this project has the potential both to strengthen the scientific understanding of how policy-level knowledge transfer and exchange functions and to provide significantly improved advice on how to ensure evidence plays a more prominent role in public policies.

Automated analysis of information processing, kinetic independence and modular architecture in biochemical networks using MIDIA.

PubMed

Bowsher, Clive G

2011-02-15

Understanding the encoding and propagation of information by biochemical reaction networks and the relationship of such information processing properties to modular network structure is of fundamental importance in the study of cell signalling and regulation. However, a rigorous, automated approach for general biochemical networks has not been available, and high-throughput analysis has therefore been out of reach. Modularization Identification by Dynamic Independence Algorithms (MIDIA) is a user-friendly, extensible R package that performs automated analysis of how information is processed by biochemical networks. An important component is the algorithm's ability to identify exact network decompositions based on both the mass action kinetics and informational properties of the network. These modularizations are visualized using a tree structure from which important dynamic conditional independence properties can be directly read. Only partial stoichiometric information needs to be used as input to MIDIA, and neither simulations nor knowledge of rate parameters are required. When applied to a signalling network, for example, the method identifies the routes and species involved in the sequential propagation of information between its multiple inputs and outputs. These routes correspond to the relevant paths in the tree structure and may be further visualized using the Input-Output Path Matrix tool. MIDIA remains computationally feasible for the largest network reconstructions currently available and is straightforward to use with models written in Systems Biology Markup Language (SBML). The package is distributed under the GNU General Public License and is available, together with a link to browsable Supplementary Material, at http://code.google.com/p/midia. Further information is at www.maths.bris.ac.uk/~macgb/Software.html.

CUFID-query: accurate network querying through random walk based network flow estimation.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

Temporal network analysis identifies early physiological and transcriptomic indicators of mild drought in Brassica rapa

PubMed Central

Gehan, Malia A; Mockler, Todd C; Weinig, Cynthia; Ewers, Brent E

2017-01-01

The dynamics of local climates make development of agricultural strategies challenging. Yield improvement has progressed slowly, especially in drought-prone regions where annual crop production suffers from episodic aridity. Underlying drought responses are circadian and diel control of gene expression that regulate daily variations in metabolic and physiological pathways. To identify transcriptomic changes that occur in the crop Brassica rapa during initial perception of drought, we applied a co-expression network approach to associate rhythmic gene expression changes with physiological responses. Coupled analysis of transcriptome and physiological parameters over a two-day time course in control and drought-stressed plants provided temporal resolution necessary for correlation of network modules with dynamic changes in stomatal conductance, photosynthetic rate, and photosystem II efficiency. This approach enabled the identification of drought-responsive genes based on their differential rhythmic expression profiles in well-watered versus droughted networks and provided new insights into the dynamic physiological changes that occur during drought. PMID:28826479

Validation and quantification of uncertainty in coupled climate models using network analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bracco, Annalisa

We developed a fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. This approach can be applied to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation, and is substantially new within the available network methodologies developed for climate studies.more » At the first layer, gridded climate data are used to identify ‘‘areas’’, i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. The robustness of the method (i.e. the ability to separate between topological distinct fields, while identifying correctly similarities) has been extensively tested. It has been proved that it provides a reliable, fast framework for comparing and ranking the ability of climate models of reproducing observed climate patterns and their connectivity. We further developed the methodology to account for lags in the connectivity between climate patterns and refined our area identification algorithm to account for autocorrelation in the data. The new methodology based on complex network analysis has been applied to state-of-the-art climate model simulations that participated to the last IPCC (International Panel for Climate Change) assessment to verify their performances, quantify uncertainties, and uncover changes in global linkages between past and future projections. Network properties of modeled sea surface temperature and rainfall over 1956–2005 have been constrained towards observations or reanalysis data sets, and their differences quantified using two metrics. Projected changes from 2051 to 2300 under the scenario with the highest representative and extended concentration pathways (RCP8.5 and ECP8.5) have then been determined. The network of models capable of reproducing well major climate modes in the recent past, changes little during this century. In contrast, among those models the uncertainties in the projections after 2100 remain substantial, and primarily associated with divergences in the representation of the modes of variability, particularly of the El Niño Southern Oscillation (ENSO), and their connectivity, and therefore with their intrinsic predictability, more so than with differences in the mean state evolution. Additionally, we evaluated the relation between the size and the ‘strength’ of the area identified by the network analysis as corresponding to ENSO noting that only a small subset of models can reproduce realistically the observations.« less

Identification of the anti-tumor activity and mechanisms of nuciferine through a network pharmacology approach

PubMed Central

Qi, Quan; Li, Rui; Li, Hui-ying; Cao, Yu-bing; Bai, Ming; Fan, Xiao-jing; Wang, Shu-yan; Zhang, Bo; Li, Shao

2016-01-01

Aim: Nuciferine is an aporphine alkaloid extracted from lotus leaves, which is a raw material in Chinese medicinal herb for weight loss. In this study we used a network pharmacology approach to identify the anti-tumor activity of nuciferine and the underlying mechanisms. Methods: The pharmacological activities and mechanisms of nuciferine were identified through target profile prediction, clustering analysis and functional enrichment analysis using our traditional Chinese medicine (TCM) network pharmacology platform. The anti-tumor activity of nuciferine was validated by in vitro and in vivo experiments. The anti-tumor mechanisms of nuciferine were predicted through network target analysis and verified by in vitro experiments. Results: The nuciferine target profile was enriched with signaling pathways and biological functions, including “regulation of lipase activity”, “response to nicotine” and “regulation of cell proliferation”. Target profile clustering results suggested that nuciferine to exert anti-tumor effect. In experimental validation, nuciferine (0.8 mg/mL) markedly inhibited the viability of human neuroblastoma SY5Y cells and mouse colorectal cancer CT26 cells in vitro, and nuciferine (0.05 mg/mL) significantly suppressed the invasion of 6 cancer cell lines in vitro. Intraperitoneal injection of nuciferine (9.5 mg/mL, ip, 3 times a week for 3 weeks) significantly decreased the weight of SY5Y and CT26 tumor xenografts in nude mice. Network target analysis and experimental validation in SY5Y and CT26 cells showed that the anti-tumor effect of nuciferine was mediated through inhibiting the PI3K-AKT signaling pathway and IL-1 levels in SY5Y and CT26 cells. Conclusion: By using a TCM network pharmacology method, nuciferine is identified as an anti-tumor agent against human neuroblastoma and mouse colorectal cancer in vitro and in vivo, through inhibiting the PI3K-AKT signaling pathways and IL-1 levels. PMID:27180984

On designing of a low leakage patient-centric provider network.

PubMed

Zheng, Yuchen; Lin, Kun; White, Thomas; Pickreign, Jeremy; Yuen-Reed, Gigi

2018-03-27

When a patient in a provider network seeks services outside of their community, the community experiences a leakage. Leakage is undesirable as it typically leads to higher out-of-network cost for patient and increases barrier for care coordination, which is particularly problematic for Accountable Care Organization (ACO) as the in-network providers are financially responsible for quality of care and outcome. We aim to design a data-driven method to identify naturally occurring provider networks driven by diabetic patient choices, and understand the relationship among provider composition, patient composition, and service leakage pattern. By doing so, we learn the features of low service leakage provider networks that can be generalized to different patient population. Data used for this study include de-identified healthcare insurance administrative data acquired from Capital District Physicians' Health Plan (CDPHP) for diabetic patients who resided in four New York state counties (Albany, Rensselaer, Saratoga, and Schenectady) in 2014. We construct a healthcare provider network based on patients' historical medical insurance claims. A community detection algorithm is used to identify naturally occurring communities of collaborating providers. For each detected community, a profile is built using several new key measures to elucidate stakeholders of our findings. Finally, import-export analysis is conducted to benchmark their leakage pattern and identify further leakage reduction opportunity. The design yields six major provider communities with diverse profiles. Some communities are geographically concentrated, while others tend to draw patients with certain diabetic co-morbidities. Providers from the same healthcare institution are likely to be assigned to the same community. While most communities have high within-community utilization and spending, at 85% and 86% respectively, leakage still persists. Hence, we utilize a metric from import-export analysis to detect leakage, gaining insight on how to minimize leakage. We identify patient-driven provider organization by surfacing providers who share a large number of patients. By analyzing the import-export behavior of each identified community using a novel approach and profiling community patient and provider composition we understand the key features of having a balanced number of PCP and specialists and provider heterogeneity.

Ganges-Brahmaputra-Meghna Delta Connectivity Analysis Using New Tools for the Automatic Extraction of Channel Networks from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Jarriel, T. M.; Isikdogan, F.; Passalacqua, P.; Bovik, A.

2017-12-01

River deltas are one of the environmental ecosystems most threatened by climate change and anthropogenic activity. While their low elevation gradients and fertile soil have made them optimal for human inhabitation and diverse ecologic growth, it also makes them susceptible to adverse effects of sea level rise, flooding, subsidence, and manmade structures such as dams, levees, and dikes. One particularly large and threatened delta that is the focus area of this study, is the Ganges-Brahmaputra-Meghna Delta (GBMD) on the southern coast of Bangladesh/West Bengal India. In this study we analyze the GBMD channel network, identify areas of maximum change of the network, and use this information to predict how the network will respond under future scenarios. Landsat images of the delta from 1973 to 2017 are analyzed using new tools for the automatic extraction of channel networks from remotely sensed imagery [Isikdogan et al., 2017a, Isikdogan et al., 2017b]. The tools return channel width and channel centerline location at the resolution of the input imagery (30 m). Channel location variance over time is computed using the combined data from 1973 to 2017 and, based on this information, zones of highest change in the system are identified (Figure 1). Network metrics measuring characteristics of the delta's channels and islands are calculated for each year of the study and compared to the variance results in order to identify what metrics capture this change. These results provide both a method to identify zones of the GBMD that are currently experiencing the most change, as well as a means to predict what areas of the delta will experience network changes in the future. This information will be useful for informing coastal sustainability decisions about what areas of such a large and complex network should be the focus of remediation and mitigation efforts. Isikdogan, F., A. Bovik, P. Passalacqua (2017a), RivaMap: An Automated River Analysis and Mapping Engine, Remote Sensing of Environment, in press. Isikdogan, F., A. Bovik, P. Passalacqua (2017b), River Network Extraction by Deep Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters, under review.

An association network analysis among microeukaryotes and bacterioplankton reveals algal bloom dynamics.

PubMed

Tan, Shangjin; Zhou, Jin; Zhu, Xiaoshan; Yu, Shichen; Zhan, Wugen; Wang, Bo; Cai, Zhonghua

2015-02-01

Algal blooms are a worldwide phenomenon and the biological interactions that underlie their regulation are only just beginning to be understood. It is established that algal microorganisms associate with many other ubiquitous, oceanic organisms, but the interactions that lead to the dynamics of bloom formation are currently unknown. To address this gap, we used network approaches to investigate the association patterns among microeukaryotes and bacterioplankton in response to a natural Scrippsiella trochoidea bloom. This is the first study to apply network approaches to bloom dynamics. To this end, terminal restriction fragment (T-RF) length polymorphism analysis showed dramatic changes in community compositions of microeukaryotes and bacterioplankton over the blooming period. A variance ratio test revealed significant positive overall associations both within and between microeukaryotic and bacterioplankton communities. An association network generated from significant correlations between T-RFs revealed that S. trochoidea had few connections to other microeukaryotes and bacterioplankton and was placed on the edge. This lack of connectivity allowed for the S. trochoidea sub-network to break off from the overall network. These results allowed us to propose a conceptual model for explaining how changes in microbial associations regulate the dynamics of an algal bloom. In addition, key T-RFs were screened by principal components analysis, correlation coefficients, and network analysis. Dominant T-RFs were then identified through 18S and 16S rRNA gene clone libraries. Results showed that microeukaryotes clustered predominantly with Dinophyceae and Perkinsea while the majority of bacterioplankton identified were Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes. The ecologi-cal roles of both were discussed in the context of these findings. © 2014 Phycological Society of America.

Ego Network Analysis of Upper Division Physics Student Survey

NASA Astrophysics Data System (ADS)

Brewe, Eric

2017-01-01

We present the analysis of student networks derived from a survey of upper division physics students. Ego networks focus on the connections that center on one person (the ego). The ego networks in this talk come from a survey that is part of an overall project focused on understanding student retention and persistence. The theory underlying this work is that social and academic integration are essential components to supporting students continued enrollment and ultimately graduation. This work uses network analysis as a way to investigate the role of social and academic interactions in retention and persistence decisions. We focus on student interactions with peers, on mentoring interactions with physics department faculty, and on engagement in physics groups and how they influence persistence. Our results, which are preliminary, will help frame the ongoing research project and identify ways in which departments can support students. This work supported by NSF grant #PHY 1344247.

Assembling the puzzle for promoting physical activity in Brazil: a social network analysis.

PubMed

Brownson, Ross C; Parra, Diana C; Dauti, Marsela; Harris, Jenine K; Hallal, Pedro C; Hoehner, Christine; Malta, Deborah Carvalho; Reis, Rodrigo S; Ramos, Luiz Roberto; Ribeiro, Isabela C; Soares, Jesus; Pratt, Michael

2010-07-01

Physical inactivity is a significant public health problem in Brazil that may be addressed by partnerships and networks. In conjunction with Project GUIA (Guide for Useful Interventions for Physical Activity in Brazil and Latin America), the aim of this study was to conduct a social network analysis of physical activity in Brazil. An online survey was completed by 28 of 35 organizations contacted from December 2008 through March 2009. Network analytic methods examined measures of collaboration, importance, leadership, and attributes of the respondent and organization. Leadership nominations for organizations studied ranged from 0 to 23. Positive predictors of collaboration included: south region, GUIA membership, years working in physical activity, and research, education, and promotion/practice areas of physical activity. The most frequently reported barrier to collaboration was bureaucracy. Social network analysis identified factors that are likely to improve collaboration among organizations in Brazil.

Visualizing collaborative electronic health record usage for hospitalized patients with heart failure.

PubMed

Soulakis, Nicholas D; Carson, Matthew B; Lee, Young Ji; Schneider, Daniel H; Skeehan, Connor T; Scholtens, Denise M

2015-03-01

To visualize and describe collaborative electronic health record (EHR) usage for hospitalized patients with heart failure. We identified records of patients with heart failure and all associated healthcare provider record usage through queries of the Northwestern Medicine Enterprise Data Warehouse. We constructed a network by equating access and updates of a patient's EHR to a provider-patient interaction. We then considered shared patient record access as the basis for a second network that we termed the provider collaboration network. We calculated network statistics, the modularity of provider interactions, and provider cliques. We identified 548 patient records accessed by 5113 healthcare providers in 2012. The provider collaboration network had 1504 nodes and 83 998 edges. We identified 7 major provider collaboration modules. Average clique size was 87.9 providers. We used a graph database to demonstrate an ad hoc query of our provider-patient network. Our analysis suggests a large number of healthcare providers across a wide variety of professions access records of patients with heart failure during their hospital stay. This shared record access tends to take place not only in a pairwise manner but also among large groups of providers. EHRs encode valuable interactions, implicitly or explicitly, between patients and providers. Network analysis provided strong evidence of multidisciplinary record access of patients with heart failure across teams of 100+ providers. Further investigation may lead to clearer understanding of how record access information can be used to strategically guide care coordination for patients hospitalized for heart failure. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Research synergy and drug development: Bright stars in neighboring constellations.

PubMed

Keserci, Samet; Livingston, Eric; Wan, Lingtian; Pico, Alexander R; Chacko, George

2017-11-01

Drug discovery and subsequent availability of a new breakthrough therapeutic or 'cure' is a compelling example of societal benefit from research advances. These advances are invariably collaborative, involving the contributions of many scientists to a discovery network in which theory and experiment are built upon. To document and understand such scientific advances, data mining of public and commercial data sources coupled with network analysis can be used as a digital methodology to assemble and analyze component events in the history of a therapeutic. This methodology is extensible beyond the history of therapeutics and its use more generally supports (i) efficiency in exploring the scientific history of a research advance (ii) documenting and understanding collaboration (iii) portfolio analysis, planning and optimization (iv) communication of the societal value of research. Building upon prior art, we have conducted a case study of five anti-cancer therapeutics to identify the collaborations that resulted in the successful development of these therapeutics both within and across their respective networks. We have linked the work of over 235,000 authors in roughly 106,000 scientific publications that capture the research crucial for the development of these five therapeutics. Applying retrospective citation discovery, we have identified a core set of publications cited in the networks of all five therapeutics and additional intersections in combinations of networks. We have enriched the content of these networks by annotating them with information on research awards from the US National Institutes of Health (NIH). Lastly, we have mapped these awards to their cognate peer review panels, identifying another layer of collaborative scientific activity that influenced the research represented in these networks.

A protein interaction network analysis for yeast integral membrane protein.

PubMed

Shi, Ming-Guang; Huang, De-Shuang; Li, Xue-Ling

2008-01-01

Although the yeast Saccharomyces cerevisiae is the best exemplified single-celled eukaryote, the vast number of protein-protein interactions of integral membrane proteins of Saccharomyces cerevisiae have not been characterized by experiments. Here, based on the kernel method of Greedy Kernel Principal Component analysis plus Linear Discriminant Analysis, we identify 300 protein-protein interactions involving 189 membrane proteins and get the outcome of a highly connected protein-protein interactions network. Furthermore, we study the global topological features of integral membrane proteins network of Saccharomyces cerevisiae. These results give the comprehensive description of protein-protein interactions of integral membrane proteins and reveal global topological and robustness of the interactome network at a system level. This work represents an important step towards a comprehensive understanding of yeast protein interactions.

Grey-matter network disintegration as predictor of cognitive and motor function with aging.

PubMed

Koini, Marisa; Duering, Marco; Gesierich, Benno G; Rombouts, Serge A R B; Ropele, Stefan; Wagner, Fabian; Enzinger, Christian; Schmidt, Reinhold

2018-06-01

Loss of grey-matter volume with advancing age affects the entire cortex. It has been suggested that atrophy occurs in a network-dependent manner with advancing age rather than in independent brain areas. The relationship between networks of structural covariance (SCN) disintegration and cognitive functioning during normal aging is not fully explored. We, therefore, aimed to (1) identify networks that lose GM integrity with advancing age, (2) investigate if age-related impairment of integrity in GM networks associates with cognitive function and decreasing fine motor skills (FMS), and (3) examine if GM disintegration is a mediator between age and cognition and FMS. T1-weighted scans of n = 257 participants (age range: 20-87) were used to identify GM networks using independent component analysis. Random forest analysis was implemented to examine the importance of network integrity as predictors of memory, executive functions, and FMS. The associations between GM disintegration, age and cognitive performance, and FMS were assessed using mediation analyses. Advancing age was associated with decreasing cognitive performance and FMS. Fourteen of 20 GM networks showed integrity changes with advancing age. Next to age and education, eight networks (fronto-parietal, fronto-occipital, temporal, limbic, secondary somatosensory, cuneal, sensorimotor network, and a cerebellar network) showed an association with cognition and FMS (up to 15.08%). GM networks partially mediated the effect between age and cognition and age and FMS. We confirm an age-related decline in cognitive functioning and FMS in non-demented community-dwelling subjects and showed that aging selectively affects the integrity of GM networks. The negative effect of age on cognition and FMS is associated with distinct GM networks and is partly mediated by their disintegration.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abercrombie, Robert K; Udoeyop, Akaninyene W

This work examines a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, worldwide patents, news archives,more » and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain we investigated.« less

Identifying Node Role in Social Network Based on Multiple Indicators

PubMed Central

Huang, Shaobin; Lv, Tianyang; Zhang, Xizhe; Yang, Yange; Zheng, Weimin; Wen, Chao

2014-01-01

It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role. PMID:25089823

A Functional and Regulatory Network Associated with PIP Expression in Human Breast Cancer

PubMed Central

Debily, Marie-Anne; Marhomy, Sandrine El; Boulanger, Virginie; Eveno, Eric; Mariage-Samson, Régine; Camarca, Alessandra; Auffray, Charles; Piatier-Tonneau, Dominique; Imbeaud, Sandrine

2009-01-01

Background The PIP (prolactin-inducible protein) gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes. Principal Findings Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP−] cells were identified. Functional and regulatory network analyses based on a knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The network identified appears associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines, and contains many genes with a STAT5 regulatory motif in their promoters. Conclusions Our global exploratory approach identified biological pathways modulated along with PIP expression, providing further support for its good prognostic value of disease-free survival in breast cancer. Moreover, our data pointed to the importance of a regulatory subnetwork associated with PIP expression in which STAT5 appears as a potential transcriptional regulator. PMID:19262752

Human parietofrontal networks related to action observation detected at rest.

PubMed

Molinari, Elisa; Baraldi, Patrizia; Campanella, Martina; Duzzi, Davide; Nocetti, Luca; Pagnoni, Giuseppe; Porro, Carlo A

2013-01-01

Recent data show a broad correspondence between human resting-state and task-related brain networks. We performed a functional magnetic resonance imaging (fMRI) study to compare, in the same subjects, the spatial independent component analysis (ICA) maps obtained at rest and during the observation of either reaching/grasping hand actions or matching static pictures. Two parietofrontal networks were identified by ICA from action observation task data. One network, specific to reaching/grasping observation, included portions of the anterior intraparietal cortex and of the dorsal and ventral lateral premotor cortices. A second network included more posterior portions of the parietal lobe, the dorsomedial frontal cortex, and more anterior and ventral parts, respectively, of the dorsal and ventral premotor cortices, extending toward Broca's area; this network was more generally related to the observation of hand action and static pictures. A good spatial correspondence was found between the 2 observation-related ICA maps and 2 ICA maps identified from resting-state data. The anatomical connectivity among the identified clusters was tested in the same volunteers, using persistent angular structure-MRI and deterministic tractography. These findings extend available knowledge of human parietofrontal circuits and further support the hypothesis of a persistent coherence within functionally relevant networks during rest.

Parameterized centrality metric for network analysis

NASA Astrophysics Data System (ADS)

Ghosh, Rumi; Lerman, Kristina

2011-06-01

A variety of metrics have been proposed to measure the relative importance of nodes in a network. One of these, alpha-centrality [P. Bonacich, Am. J. Sociol.0002-960210.1086/228631 92, 1170 (1987)], measures the number of attenuated paths that exist between nodes. We introduce a normalized version of this metric and use it to study network structure, for example, to rank nodes and find community structure of the network. Specifically, we extend the modularity-maximization method for community detection to use this metric as the measure of node connectivity. Normalized alpha-centrality is a powerful tool for network analysis, since it contains a tunable parameter that sets the length scale of interactions. Studying how rankings and discovered communities change when this parameter is varied allows us to identify locally and globally important nodes and structures. We apply the proposed metric to several benchmark networks and show that it leads to better insights into network structure than alternative metrics.

Scientific authorship and collaboration network analysis on malaria research in Benin: papers indexed in the web of science (1996-2016).

PubMed

Azondekon, Roseric; Harper, Zachary James; Agossa, Fiacre Rodrigue; Welzig, Charles Michael; McRoy, Susan

2018-01-01

To sustain the critical progress made, prioritization and a multidisciplinary approach to malaria research remain important to the national malaria control program in Benin. To document the structure of the malaria collaborative research in Benin, we analyze authorship of the scientific documents published on malaria from Benin. We collected bibliographic data from the Web Of Science on malaria research in Benin from January 1996 to December 2016. From the collected data, a mulitigraph co-authorship network with authors representing vertices was generated. An edge was drawn between two authors when they co-author a paper. We computed vertex degree, betweenness, closeness, and eigenvectors among others to identify prolific authors. We further assess the weak points and how information flow in the network. Finally, we perform a hierarchical clustering analysis, and Monte-Carlo simulations. Overall, 427 publications were included in this study. The generated network contained 1792 authors and 116,388 parallel edges which converted in a weighted graph of 1792 vertices and 95,787 edges. Our results suggested that prolific authors with higher degrees tend to collaborate more. The hierarchical clustering revealed 23 clusters, seven of which form a giant component containing 94% of all the vertices in the network. This giant component has all the characteristics of a small-world network with a small shortest path distance between pairs of three, a diameter of 10 and a high clustering coefficient of 0.964. However, Monte-Carlo simulations suggested our observed network is an unusual type of small-world network. Sixteen vertices were identified as weak articulation points within the network. The malaria research collaboration network in Benin is a complex network that seems to display the characteristics of a small-world network. This research reveals the presence of closed research groups where collaborative research likely happens only between members. Interdisciplinary collaboration tends to occur at higher levels between prolific researchers. Continuously supporting, stabilizing the identified key brokers and most productive authors in the Malaria research collaborative network is an urgent need in Benin. It will foster the malaria research network and ensure the promotion of junior scientists in the field.

On structural identifiability analysis of the cascaded linear dynamic systems in isotopically non-stationary 13C labelling experiments.

PubMed

Lin, Weilu; Wang, Zejian; Huang, Mingzhi; Zhuang, Yingping; Zhang, Siliang

2018-06-01

The isotopically non-stationary 13C labelling experiments, as an emerging experimental technique, can estimate the intracellular fluxes of the cell culture under an isotopic transient period. However, to the best of our knowledge, the issue of the structural identifiability analysis of non-stationary isotope experiments is not well addressed in the literature. In this work, the local structural identifiability analysis for non-stationary cumomer balance equations is conducted based on the Taylor series approach. The numerical rank of the Jacobian matrices of the finite extended time derivatives of the measured fractions with respect to the free parameters is taken as the criterion. It turns out that only one single time point is necessary to achieve the structural identifiability analysis of the cascaded linear dynamic system of non-stationary isotope experiments. The equivalence between the local structural identifiability of the cascaded linear dynamic systems and the local optimum condition of the nonlinear least squares problem is elucidated in the work. Optimal measurements sets can then be determined for the metabolic network. Two simulated metabolic networks are adopted to demonstrate the utility of the proposed method. Copyright © 2018 Elsevier Inc. All rights reserved.

Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems

PubMed Central

Smith, Jason F.; Pillai, Ajay; Chen, Kewei; Horwitz, Barry

2012-01-01

Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a “node” in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an “instantaneous” connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis. PMID:22279430

IL-32 is a molecular marker of a host defense network in human tuberculosis

PubMed Central

Montoya, Dennis; Inkeles, Megan S.; Liu, Phillip T.; Realegeno, Susan; Teles, Rosane M. B.; Vaidya, Poorva; Munoz, Marcos A.; Schenk, Mirjam; Swindell, William R.; Chun, Rene; Zavala, Kathryn; Hewison, Martin; Adams, John S.; Horvath, Steve; Pellegrini, Matteo; Bloom, Barry R.; Modlin, Robert L.

2014-01-01

Tuberculosis is a leading cause of infectious disease–related death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease. Factors that contribute to protection could prove to be promising targets for M. tuberculosis therapies. Analysis of peripheral blood gene expression profiles of active tuberculosis patients has identified correlates of risk for disease or pathogenesis. We sought to identify potential human candidate markers of host defense by studying gene expression profiles of macrophages, cells that, upon infection by M. tuberculosis, can mount an antimicrobial response. Weighted gene coexpression network analysis revealed an association between the cytokine interleukin-32 (IL-32) and the vitamin D antimicrobial pathway in a network of interferon-γ– and IL-15–induced “defense response” genes. IL-32 induced the vitamin D–dependent antimicrobial peptides cathelicidin and DEFB4 and to generate antimicrobial activity in vitro, dependent on the presence of adequate 25-hydroxyvitamin D. In addition, the IL-15–induced defense response macrophage gene network was integrated with ranked pairwise comparisons of gene expression from five different clinical data sets of latent compared with active tuberculosis or healthy controls and a coexpression network derived from gene expression in patients with tuberculosis undergoing chemotherapy. Together, these analyses identified eight common genes, including IL-32, as molecular markers of latent tuberculosis and the IL-15–induced gene network. As maintaining M. tuberculosis in a latent state and preventing transition to active disease may represent a form of host resistance, these results identify IL-32 as one functional marker and potential correlate of protection against active tuberculosis. PMID:25143364

IL-32 is a molecular marker of a host defense network in human tuberculosis.

PubMed

Montoya, Dennis; Inkeles, Megan S; Liu, Phillip T; Realegeno, Susan; Teles, Rosane M B; Vaidya, Poorva; Munoz, Marcos A; Schenk, Mirjam; Swindell, William R; Chun, Rene; Zavala, Kathryn; Hewison, Martin; Adams, John S; Horvath, Steve; Pellegrini, Matteo; Bloom, Barry R; Modlin, Robert L

2014-08-20

Tuberculosis is a leading cause of infectious disease-related death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease. Factors that contribute to protection could prove to be promising targets for M. tuberculosis therapies. Analysis of peripheral blood gene expression profiles of active tuberculosis patients has identified correlates of risk for disease or pathogenesis. We sought to identify potential human candidate markers of host defense by studying gene expression profiles of macrophages, cells that, upon infection by M. tuberculosis, can mount an antimicrobial response. Weighted gene coexpression network analysis revealed an association between the cytokine interleukin-32 (IL-32) and the vitamin D antimicrobial pathway in a network of interferon-γ- and IL-15-induced "defense response" genes. IL-32 induced the vitamin D-dependent antimicrobial peptides cathelicidin and DEFB4 and to generate antimicrobial activity in vitro, dependent on the presence of adequate 25-hydroxyvitamin D. In addition, the IL-15-induced defense response macrophage gene network was integrated with ranked pairwise comparisons of gene expression from five different clinical data sets of latent compared with active tuberculosis or healthy controls and a coexpression network derived from gene expression in patients with tuberculosis undergoing chemotherapy. Together, these analyses identified eight common genes, including IL-32, as molecular markers of latent tuberculosis and the IL-15-induced gene network. As maintaining M. tuberculosis in a latent state and preventing transition to active disease may represent a form of host resistance, these results identify IL-32 as one functional marker and potential correlate of protection against active tuberculosis. Copyright © 2014, American Association for the Advancement of Science.

Integrated metagenomic analysis of the rumen microbiome of cattle reveals key biological mechanisms associated with methane traits.

PubMed

Wang, Haiying; Zheng, Huiru; Browne, Fiona; Roehe, Rainer; Dewhurst, Richard J; Engel, Felix; Hemmje, Matthias; Lu, Xiangwu; Walsh, Paul

2017-07-15

Methane is one of the major contributors to global warming. The rumen microbiota is directly involved in methane production in cattle. The link between variation in rumen microbial communities and host genetics has important applications and implications in bioscience. Having the potential to reveal the full extent of microbial gene diversity and complex microbial interactions, integrated metagenomics and network analysis holds great promise in this endeavour. This study investigates the rumen microbial community in cattle through the integration of metagenomic and network-based approaches. Based on the relative abundance of 1570 microbial genes identified in a metagenomics analysis, the co-abundance network was constructed and functional modules of microbial genes were identified. One of the main contributions is to develop a random matrix theory-based approach to automatically determining the correlation threshold used to construct the co-abundance network. The resulting network, consisting of 549 microbial genes and 3349 connections, exhibits a clear modular structure with certain trait-specific genes highly over-represented in modules. More specifically, all the 20 genes previously identified to be associated with methane emissions are found in a module (hypergeometric test, p<10 -11 ). One third of genes are involved in methane metabolism pathways. The further examination of abundance profiles across 8 samples of genes highlights that the revealed pattern of metagenomics abundance has a strong association with methane emissions. Furthermore, the module is significantly enriched with microbial genes encoding enzymes that are directly involved in methanogenesis (hypergeometric test, p<10 -9 ). Copyright © 2017 Elsevier Inc. All rights reserved.

Shared molecular pathways and gene networks for cardiovascular disease and type 2 diabetes mellitus in women across diverse ethnicities.

PubMed

Chan, Kei Hang K; Huang, Yen-Tsung; Meng, Qingying; Wu, Chunyuan; Reiner, Alexander; Sobel, Eric M; Tinker, Lesley; Lusis, Aldons J; Yang, Xia; Liu, Simin

2014-12-01

Although cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D) share many common risk factors, potential molecular mechanisms that may also be shared for these 2 disorders remain unknown. Using an integrative pathway and network analysis, we performed genome-wide association studies in 8155 blacks, 3494 Hispanic American, and 3697 Caucasian American women who participated in the national Women's Health Initiative single-nucleotide polymorphism (SNP) Health Association Resource and the Genomics and Randomized Trials Network. Eight top pathways and gene networks related to cardiomyopathy, calcium signaling, axon guidance, cell adhesion, and extracellular matrix seemed to be commonly shared between CVD and T2D across all 3 ethnic groups. We also identified ethnicity-specific pathways, such as cell cycle (specific for Hispanic American and Caucasian American) and tight junction (CVD and combined CVD and T2D in Hispanic American). In network analysis of gene-gene or protein-protein interactions, we identified key drivers that included COL1A1, COL3A1, and ELN in the shared pathways for both CVD and T2D. These key driver genes were cross-validated in multiple mouse models of diabetes mellitus and atherosclerosis. Our integrative analysis of American women of 3 ethnicities identified multiple shared biological pathways and key regulatory genes for the development of CVD and T2D. These prospective findings also support the notion that ethnicity-specific susceptibility genes and process are involved in the pathogenesis of CVD and T2D. © 2014 American Heart Association, Inc.

Stochastic flux analysis of chemical reaction networks

PubMed Central

2013-01-01

Background Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. Results We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. Conclusions We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. PMID:24314153

Stochastic flux analysis of chemical reaction networks.

PubMed

Kahramanoğulları, Ozan; Lynch, James F

2013-12-07

Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.

Narcissism and Social Networking Behavior: A Meta-Analysis.

PubMed

Gnambs, Timo; Appel, Markus

2018-04-01

The increasing popularity of social networking sites (SNS) such as Facebook and Twitter has given rise to speculations that the intensity of using these platforms is associated with narcissistic tendencies. However, recent research on this issue has been all but conclusive. We present a three-level, random effects meta-analysis including 289 effect sizes from 57 studies (total N = 25,631) on the association between trait narcissism and social networking behavior. The meta-analysis identified a small to moderate effect of ρ = .17 (τ = .11), 95% CI [.13, .21], for grandiose narcissism that replicated across different social networking platforms, respondent characteristics, and time. Moderator analyses revealed pronounced cultural differences, with stronger associations in power-distant cultures. Moreover, social networking behaviors geared toward self-presentation and the number of SNS friends exhibited stronger effects than usage durations. Overall, the study not only supported but also refined the notion of a relationship between engaging in social networking sites and narcissistic personality traits. © 2017 Wiley Periodicals, Inc.

Real-Time Visualization of Network Behaviors for Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Best, Daniel M.; Bohn, Shawn J.; Love, Douglas V.

Plentiful, complex, and dynamic data make understanding the state of an enterprise network difficult. Although visualization can help analysts understand baseline behaviors in network traffic and identify off-normal events, visual analysis systems often do not scale well to operational data volumes (in the hundreds of millions to billions of transactions per day) nor to analysis of emergent trends in real-time data. We present a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi. This system provides situational understanding of real-time network activity to help analysts takemore » proactive response steps. We have developed these techniques using requirements gathered from the government users for which the tools are being developed. By linking multiple visualization tools to a streaming analytic pipeline, and designing each tool to support a particular kind of analysis (from high-level awareness to detailed investigation), analysts can understand the behavior of a network across multiple levels of abstraction.« less

Connections, Paths, and Explanations--A Social Network Approach to Investigating Experiences of Early Childhood Special Education with the ECLS-K

ERIC Educational Resources Information Center

Akers, Kathryn Shirley

2011-01-01

The purpose of this study is to demonstrate a practical application of social network analysis in the field of education using a large-scale data source. Using the Early Childhood Longitudinal Base Year data, a network is identified by examining the connections that occur between supports, both inside and outside formal special education resources…

Mobilizing Ideas in Knowledge Networks: A Social Network Analysis of the Human Resource Management Community 1990-2005

ERIC Educational Resources Information Center

Henneberg, Stephan C.; Swart, Juani; Naude, Peter; Jiang, Zhizhong; Mouzas, Stefanos

2009-01-01

Purpose: The purpose of this paper is to show the role of social networks in mobilizing how actors both impact and are impacted on by their colleagues. It seeks to compare the human resource management (HRM) academic community with two other comparable communities, and to identify those groups that are seen to work closely together.…

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

PubMed Central

Kovács, István A.; Palotai, Robin; Szalay, Máté S.; Csermely, Peter

2010-01-01

Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction. PMID:20824084

Identification of key target genes and pathways in laryngeal carcinoma

PubMed Central

Liu, Feng; Du, Jintao; Liu, Jun; Wen, Bei

2016-01-01

The purpose of the present study was to screen the key genes associated with laryngeal carcinoma and to investigate the molecular mechanism of laryngeal carcinoma progression. The gene expression profile of GSE10935 [Gene Expression Omnibus (GEO) accession number], including 12 specimens from laryngeal papillomas and 12 specimens from normal laryngeal epithelia controls, was downloaded from the GEO database. Differentially expressed genes (DEGs) were screened in laryngeal papillomas compared with normal controls using Limma package in R language, followed by Gene Ontology (GO) enrichment analysis and pathway enrichment analysis. Furthermore, the protein-protein interaction (PPI) network of DEGs was constructed using Cytoscape software and modules were analyzed using MCODE plugin from the PPI network. Furthermore, significant biological pathway regions (sub-pathway) were identified by using iSubpathwayMiner analysis. A total of 67 DEGs were identified, including 27 up-regulated genes and 40 down-regulated genes and they were involved in different GO terms and pathways. PPI network analysis revealed that Ras association (RalGDS/AF-6) domain family member 1 (RASSF1) was a hub protein. The sub-pathway analysis identified 9 significantly enriched sub-pathways, including glycolysis/gluconeogenesis and nitrogen metabolism. Genes such as phosphoglycerate kinase 1 (PGK1), carbonic anhydrase II (CA2), and carbonic anhydrase XII (CA12) whose node degrees were >10 were identified in the disease risk sub-pathway. Genes in the sub-pathway, such as RASSF1, PGK1, CA2 and CA12 were presumed to serve critical roles in laryngeal carcinoma. The present study identified DEGs and their sub-pathways in the disease, which may serve as potential targets for treatment of laryngeal carcinoma. PMID:27446427

Identifying Key Actors in Heterogeneous Networks

DTIC Science & Technology

2017-11-29

analysis (SNA) and game theory (GT) to improve accuracy for detecting significant or “powerful” actors within a total actor space when both resource...coalesce in order to achieve a desired outcome. Cooperative game theory (CGT) models of coalition formation are based on two limiting assumptions: that...demonstration of a new approach for synthesizing social network analysis and game theory. The ultimate goal of this research agenda is to generalize

Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis

PubMed Central

Huang, Jie; Sun, Zhandong; Yan, Wenying; Zhu, Yujie; Lin, Yuxin; Chen, Jiajai; Shen, Bairong

2014-01-01

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers. PMID:24809055

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis.

PubMed

Jeong, Hyeri; Kim, Jongwoon; Kim, Youngjun

2017-09-30

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer.

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

NASA Astrophysics Data System (ADS)

Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

2018-04-01

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach.

PubMed

Zhen, Cheng; Zhu, Caizhong; Chen, Haoyang; Xiong, Yiru; Tan, Junyuan; Chen, Dong; Li, Jin

2017-02-21

To systematically explore the molecular mechanism for hepatocellular carcinoma (HCC) metastasis and identify regulatory genes with text mining methods. Genes with highest frequencies and significant pathways related to HCC metastasis were listed. A handful of proteins such as EGFR, MDM2, TP53 and APP, were identified as hub nodes in PPI (protein-protein interaction) network. Compared with unique genes for HBV-HCCs, genes particular to HCV-HCCs were less, but may participate in more extensive signaling processes. VEGFA, PI3KCA, MAPK1, MMP9 and other genes may play important roles in multiple phenotypes of metastasis. Genes in abstracts of HCC-metastasis literatures were identified. Word frequency analysis, KEGG pathway and PPI network analysis were performed. Then co-occurrence analysis between genes and metastasis-related phenotypes were carried out. Text mining is effective for revealing potential regulators or pathways, but the purpose of it should be specific, and the combination of various methods will be more useful.

A systems biology approach to detect key pathways and interaction networks in gastric cancer on the basis of microarray analysis.

PubMed

Guo, Leilei; Song, Chunhua; Wang, Peng; Dai, Liping; Zhang, Jianying; Wang, Kaijuan

2015-11-01

The aim of the present study was to explore key molecular pathways contributing to gastric cancer (GC) and to construct an interaction network between significant pathways and potential biomarkers. Publicly available gene expression profiles of GSE29272 for GC, and data for the corresponding normal tissue, were downloaded from Gene Expression Omnibus. Pre‑processing and differential analysis were performed with R statistical software packages, and a number of differentially expressed genes (DEGs) were obtained. A functional enrichment analysis was performed for all the DEGs with a BiNGO plug‑in in Cytoscape. Their correlation was analyzed in order to construct a network. The modularity analysis and pathway identification operations were used to identify graph clusters and associated pathways. The underlying molecular mechanisms involving these DEGs were also assessed by data mining. A total of 249 DEGs, which were markedly upregulated and downregulated, were identified. The extracellular region contained the most significantly over‑represented functional terms, with respect to upregulated and downregulated genes, and the closest topological matches were identified for taste transduction and regulation of autophagy. In addition, extracellular matrix‑receptor interactions were identified as the most relevant pathway associated with the progression of GC. The genes for fibronectin 1, secreted phosphoprotein 1, collagen type 4 variant α‑1/2 and thrombospondin 1, which are involved in the pathways, may be considered as potential therapeutic targets for GC. A series of associations between candidate genes and key pathways were also identified for GC, and their correlation may provide novel insights into the pathogenesis of GC.

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

PubMed Central

Li, Jingjing; Shi, Minyi; Ma, Zhihai; Zhao, Shuchun; Euskirchen, Ghia; Ziskin, Jennifer; Urban, Alexander; Hallmayer, Joachim; Snyder, Michael

2014-01-01

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology. PMID:25549968

The dynamics of information-driven coordination phenomena: A transfer entropy analysis

PubMed Central

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-01-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data. PMID:27051875

The dynamics of information-driven coordination phenomena: A transfer entropy analysis.

PubMed

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-04-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Resting state network topology of the ferret brain.

PubMed

Zhou, Zhe Charles; Salzwedel, Andrew P; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K; Gilmore, John H; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-12-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4T MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. Copyright © 2016 Elsevier Inc. All rights reserved.

Computational analysis of multimorbidity between asthma, eczema and rhinitis

PubMed Central

Aguilar, Daniel; Pinart, Mariona; Koppelman, Gerard H.; Saeys, Yvan; Nawijn, Martijn C.; Postma, Dirkje S.; Akdis, Mübeccel; Auffray, Charles; Ballereau, Stéphane; Benet, Marta; García-Aymerich, Judith; González, Juan Ramón; Guerra, Stefano; Keil, Thomas; Kogevinas, Manolis; Lambrecht, Bart; Lemonnier, Nathanael; Melen, Erik; Sunyer, Jordi; Valenta, Rudolf; Valverde, Sergi; Wickman, Magnus; Bousquet, Jean; Oliva, Baldo; Antó, Josep M.

2017-01-01

Background The mechanisms explaining the co-existence of asthma, eczema and rhinitis (allergic multimorbidity) are largely unknown. We investigated the mechanisms underlying multimorbidity between three main allergic diseases at a molecular level by identifying the proteins and cellular processes that are common to them. Methods An in silico study based on computational analysis of the topology of the protein interaction network was performed in order to characterize the molecular mechanisms of multimorbidity of asthma, eczema and rhinitis. As a first step, proteins associated to either disease were identified using data mining approaches, and their overlap was calculated. Secondly, a functional interaction network was built, allowing to identify cellular pathways involved in allergic multimorbidity. Finally, a network-based algorithm generated a ranked list of newly predicted multimorbidity-associated proteins. Results Asthma, eczema and rhinitis shared a larger number of associated proteins than expected by chance, and their associated proteins exhibited a significant degree of interconnectedness in the interaction network. There were 15 pathways involved in the multimorbidity of asthma, eczema and rhinitis, including IL4 signaling and GATA3-related pathways. A number of proteins potentially associated to these multimorbidity processes were also obtained. Conclusions These results strongly support the existence of an allergic multimorbidity cluster between asthma, eczema and rhinitis, and suggest that type 2 signaling pathways represent a relevant multimorbidity mechanism of allergic diseases. Furthermore, we identified new candidates contributing to multimorbidity that may assist in identifying new targets for multimorbid allergic diseases. PMID:28598986

Computational analysis of multimorbidity between asthma, eczema and rhinitis.

PubMed

Aguilar, Daniel; Pinart, Mariona; Koppelman, Gerard H; Saeys, Yvan; Nawijn, Martijn C; Postma, Dirkje S; Akdis, Mübeccel; Auffray, Charles; Ballereau, Stéphane; Benet, Marta; García-Aymerich, Judith; González, Juan Ramón; Guerra, Stefano; Keil, Thomas; Kogevinas, Manolis; Lambrecht, Bart; Lemonnier, Nathanael; Melen, Erik; Sunyer, Jordi; Valenta, Rudolf; Valverde, Sergi; Wickman, Magnus; Bousquet, Jean; Oliva, Baldo; Antó, Josep M

2017-01-01

The mechanisms explaining the co-existence of asthma, eczema and rhinitis (allergic multimorbidity) are largely unknown. We investigated the mechanisms underlying multimorbidity between three main allergic diseases at a molecular level by identifying the proteins and cellular processes that are common to them. An in silico study based on computational analysis of the topology of the protein interaction network was performed in order to characterize the molecular mechanisms of multimorbidity of asthma, eczema and rhinitis. As a first step, proteins associated to either disease were identified using data mining approaches, and their overlap was calculated. Secondly, a functional interaction network was built, allowing to identify cellular pathways involved in allergic multimorbidity. Finally, a network-based algorithm generated a ranked list of newly predicted multimorbidity-associated proteins. Asthma, eczema and rhinitis shared a larger number of associated proteins than expected by chance, and their associated proteins exhibited a significant degree of interconnectedness in the interaction network. There were 15 pathways involved in the multimorbidity of asthma, eczema and rhinitis, including IL4 signaling and GATA3-related pathways. A number of proteins potentially associated to these multimorbidity processes were also obtained. These results strongly support the existence of an allergic multimorbidity cluster between asthma, eczema and rhinitis, and suggest that type 2 signaling pathways represent a relevant multimorbidity mechanism of allergic diseases. Furthermore, we identified new candidates contributing to multimorbidity that may assist in identifying new targets for multimorbid allergic diseases.

Gaussian Graphical Models Identify Networks of Dietary Intake in a German Adult Population.

PubMed

Iqbal, Khalid; Buijsse, Brian; Wirth, Janine; Schulze, Matthias B; Floegel, Anna; Boeing, Heiner

2016-03-01

Data-reduction methods such as principal component analysis are often used to derive dietary patterns. However, such methods do not assess how foods are consumed in relation to each other. Gaussian graphical models (GGMs) are a set of novel methods that can address this issue. We sought to apply GGMs to derive sex-specific dietary intake networks representing consumption patterns in a German adult population. Dietary intake data from 10,780 men and 16,340 women of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort were cross-sectionally analyzed to construct dietary intake networks. Food intake for each participant was estimated using a 148-item food-frequency questionnaire that captured the intake of 49 food groups. GGMs were applied to log-transformed intakes (grams per day) of 49 food groups to construct sex-specific food networks. Semiparametric Gaussian copula graphical models (SGCGMs) were used to confirm GGM results. In men, GGMs identified 1 major dietary network that consisted of intakes of red meat, processed meat, cooked vegetables, sauces, potatoes, cabbage, poultry, legumes, mushrooms, soup, and whole-grain and refined breads. For women, a similar network was identified with the addition of fried potatoes. Other identified networks consisted of dairy products and sweet food groups. SGCGMs yielded results comparable to those of GGMs. GGMs are a powerful exploratory method that can be used to construct dietary networks representing dietary intake patterns that reveal how foods are consumed in relation to each other. GGMs indicated an apparent major role of red meat intake in a consumption pattern in the studied population. In the future, identified networks might be transformed into pattern scores for investigating their associations with health outcomes. © 2016 American Society for Nutrition.

Animal welfare: a social networks perspective.

PubMed

Kleinhappel, Tanja K; John, Elizabeth A; Pike, Thomas W; Wilkinson, Anna; Burman, Oliver H P

2016-01-01

Social network theory provides a useful tool to study complex social relationships in animals. The possibility to look beyond dyadic interactions by considering whole networks of social relationships allows researchers the opportunity to study social groups in more natural ways. As such, network-based analyses provide an informative way to investigate the factors influencing the social environment of group-living animals, and so has direct application to animal welfare. For example, animal groups in captivity are frequently disrupted by separations, reintroductions and/or mixing with unfamiliar individuals and this can lead to social stress and associated aggression. Social network analysis ofanimal groups can help identify the underlying causes of these socially-derived animal welfare concerns. In this review we discuss how this approach can be applied, and how it could be used to identify potential interventions and solutions in the area of animal welfare.

Spatio-temporal organization of dynamics in a two-dimensional periodically driven vortex flow: A Lagrangian flow network perspective.

PubMed

Lindner, Michael; Donner, Reik V

2017-03-01

We study the Lagrangian dynamics of passive tracers in a simple model of a driven two-dimensional vortex resembling real-world geophysical flow patterns. Using a discrete approximation of the system's transfer operator, we construct a directed network that describes the exchange of mass between distinct regions of the flow domain. By studying different measures characterizing flow network connectivity at different time-scales, we are able to identify the location of dynamically invariant structures and regions of maximum dispersion. Specifically, our approach allows us to delimit co-existing flow regimes with different dynamics. To validate our findings, we compare several network characteristics to the well-established finite-time Lyapunov exponents and apply a receiver operating characteristic analysis to identify network measures that are particularly useful for unveiling the skeleton of Lagrangian chaos.

Gene Expression Profiling of Gastric Cancer

PubMed Central

Marimuthu, Arivusudar; Jacob, Harrys K.C.; Jakharia, Aniruddha; Subbannayya, Yashwanth; Keerthikumar, Shivakumar; Kashyap, Manoj Kumar; Goel, Renu; Balakrishnan, Lavanya; Dwivedi, Sutopa; Pathare, Swapnali; Dikshit, Jyoti Bajpai; Maharudraiah, Jagadeesha; Singh, Sujay; Sameer Kumar, Ghantasala S; Vijayakumar, M.; Veerendra Kumar, Kariyanakatte Veeraiah; Premalatha, Chennagiri Shrinivasamurthy; Tata, Pramila; Hariharan, Ramesh; Roa, Juan Carlos; Prasad, T.S.K; Chaerkady, Raghothama; Kumar, Rekha Vijay; Pandey, Akhilesh

2015-01-01

Gastric cancer is the second leading cause of cancer death worldwide, both in men and women. A genomewide gene expression analysis was carried out to identify differentially expressed genes in gastric adenocarcinoma tissues as compared to adjacent normal tissues. We used Agilent’s whole human genome oligonucleotide microarray platform representing ~41,000 genes to carry out gene expression analysis. Two-color microarray analysis was employed to directly compare the expression of genes between tumor and normal tissues. Through this approach, we identified several previously known candidate genes along with a number of novel candidate genes in gastric cancer. Testican-1 (SPOCK1) was one of the novel molecules that was 10-fold upregulated in tumors. Using tissue microarrays, we validated the expression of testican-1 by immunohistochemical staining. It was overexpressed in 56% (160/282) of the cases tested. Pathway analysis led to the identification of several networks in which SPOCK1 was among the topmost networks of interacting genes. By gene enrichment analysis, we identified several genes involved in cell adhesion and cell proliferation to be significantly upregulated while those corresponding to metabolic pathways were significantly downregulated. The differentially expressed genes identified in this study are candidate biomarkers for gastric adenoacarcinoma. PMID:27030788

Identification of functional modules using network topology and high-throughput data.

PubMed

Ulitsky, Igor; Shamir, Ron

2007-01-26

With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data. We describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity. We have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

Identification and functional analysis of risk-related microRNAs for the prognosis of patients with bladder urothelial carcinoma.

PubMed

Gao, Ji; Li, Hongyan; Liu, Lei; Song, Lide; Lv, Yanting; Han, Yuping

2017-12-01

The aim of the present study was to investigate risk-related microRNAs (miRs) for bladder urothelial carcinoma (BUC) prognosis. Clinical and microRNA expression data downloaded from the Cancer Genome Atlas were utilized for survival analysis. Risk factor estimation was performed using Cox's proportional regression analysis. A microRNA-regulated target gene network was constructed and presented using Cytoscape. In addition, the Database for Annotation, Visualization and Integrated Discovery was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, followed by protein-protein interaction (PPI) network analysis. Finally, the K-clique method was applied to analyze sub-pathways. A total of 16 significant microRNAs, including hsa-miR-3622a and hsa-miR-29a, were identified (P<0.05). Following Cox's proportional regression analysis, hsa-miR-29a was screened as a prognostic marker of BUC risk (P=0.0449). A regulation network of hsa-miR-29a comprising 417 target genes was constructed. These target genes were primarily enriched in GO terms, including collagen fibril organization, extracellular matrix (ECM) organization and pathways, such as focal adhesion (P<0.05). A PPI network including 197 genes and 510 interactions, was constructed. The top 21 genes in the network module were enriched in GO terms, including collagen fibril organization and pathways, such as ECM receptor interaction (P<0.05). Finally, 4 sub-pathways of cysteine and methionine metabolism, including paths 00270_4, 00270_1, 00270_2 and 00270_5, were obtained (P<0.01) and identified to be enriched through DNA (cytosine-5)-methyltransferase ( DNMT)3A, DNMT3B , methionine adenosyltransferase 2α ( MAT2A ) and spermine synthase ( SMS ). The identified microRNAs, particularly hsa-miR-29a and its 4 associated target genes DNMT3A, DNMT3B, MAT2A and SMS , may participate in the prognostic risk mechanism of BUC.

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.

PubMed

Lin, Yuxin; Chen, Feifei; Shen, Li; Tang, Xiaoyu; Du, Cui; Sun, Zhandong; Ding, Huijie; Chen, Jiajia; Shen, Bairong

2018-05-21

Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases. In this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets. With this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis. Five microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.

Exploiting social influence to magnify population-level behaviour change in maternal and child health: study protocol for a randomised controlled trial of network targeting algorithms in rural Honduras

PubMed Central

Shakya, Holly B; Stafford, Derek; Hughes, D Alex; Keegan, Thomas; Negron, Rennie; Broome, Jai; McKnight, Mark; Nicoll, Liza; Nelson, Jennifer; Iriarte, Emma; Ordonez, Maria; Airoldi, Edo; Fowler, James H; Christakis, Nicholas A

2017-01-01

Introduction Despite global progress on many measures of child health, rates of neonatal mortality remain high in the developing world. Evidence suggests that substantial improvements can be achieved with simple, low-cost interventions within family and community settings, particularly those designed to change knowledge and behaviour at the community level. Using social network analysis to identify structurally influential community members and then targeting them for intervention shows promise for the implementation of sustainable community-wide behaviour change. Methods and analysis We will use a detailed understanding of social network structure and function to identify novel ways of targeting influential individuals to foster cascades of behavioural change at a population level. Our work will involve experimental and observational analyses. We will map face-to-face social networks of 30 000 people in 176 villages in Western Honduras, and then conduct a randomised controlled trial of a friendship-based network-targeting algorithm with a set of well-established care interventions. We will also test whether the proportion of the population targeted affects the degree to which the intervention spreads throughout the network. We will test scalable methods of network targeting that would not, in the future, require the actual mapping of social networks but would still offer the prospect of rapidly identifying influential targets for public health interventions. Ethics and dissemination The Yale IRB and the Honduran Ministry of Health approved all data collection procedures (Protocol number 1506016012) and all participants will provide informed consent before enrolment. We will publish our findings in peer-reviewed journals as well as engage non-governmental organisations and other actors through venues for exchanging practical methods for behavioural health interventions, such as global health conferences. We will also develop a ‘toolkit’ for practitioners to use in network-based intervention efforts, including public release of our network mapping software. Trial registration number NCT02694679; Pre-results. PMID:28289044

Regular Topologies for Gigabit Wide-Area Networks. Volume 1

NASA Technical Reports Server (NTRS)

Shacham, Nachum; Denny, Barbara A.; Lee, Diane S.; Khan, Irfan H.; Lee, Danny Y. C.; McKenney, Paul

1994-01-01

In general terms, this project aimed at the analysis and design of techniques for very high-speed networking. The formal objectives of the project were to: (1) Identify switch and network technologies for wide-area networks that interconnect a large number of users and can provide individual data paths at gigabit/s rates; (2) Quantitatively evaluate and compare existing and proposed architectures and protocols, identify their strength and growth potentials, and ascertain the compatibility of competing technologies; and (3) Propose new approaches to existing architectures and protocols, and identify opportunities for research to overcome deficiencies and enhance performance. The project was organized into two parts: 1. The design, analysis, and specification of techniques and protocols for very-high-speed network environments. In this part, SRI has focused on several key high-speed networking areas, including Forward Error Control (FEC) for high-speed networks in which data distortion is the result of packet loss, and the distribution of broadband, real-time traffic in multiple user sessions. 2. Congestion Avoidance Testbed Experiment (CATE). This part of the project was done within the framework of the DARTnet experimental T1 national network. The aim of the work was to advance the state of the art in benchmarking DARTnet's performance and traffic control by developing support tools for network experimentation, by designing benchmarks that allow various algorithms to be meaningfully compared, and by investigating new queueing techniques that better satisfy the needs of best-effort and reserved-resource traffic. This document is the final technical report describing the results obtained by SRI under this project. The report consists of three volumes: Volume 1 contains a technical description of the network techniques developed by SRI in the areas of FEC and multicast of real-time traffic. Volume 2 describes the work performed under CATE. Volume 3 contains the source code of all software developed under CATE.

Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses.

PubMed

Ogawa, Takeshi; Aihara, Takatsugu; Shimokawa, Takeaki; Yamashita, Okito

2018-04-24

Creative insight occurs with an "Aha!" experience when solving a difficult problem. Here, we investigated large-scale networks associated with insight problem solving. We recruited 232 healthy participants aged 21-69 years old. Participants completed a magnetic resonance imaging study (MRI; structural imaging and a 10 min resting-state functional MRI) and an insight test battery (ITB) consisting of written questionnaires (matchstick arithmetic task, remote associates test, and insight problem solving task). To identify the resting-state functional connectivity (RSFC) associated with individual creative insight, we conducted an exploratory voxel-based morphometry (VBM)-constrained RSFC analysis. We identified positive correlations between ITB score and grey matter volume (GMV) in the right insula and middle cingulate cortex/precuneus, and a negative correlation between ITB score and GMV in the left cerebellum crus 1 and right supplementary motor area. We applied seed-based RSFC analysis to whole brain voxels using the seeds obtained from the VBM and identified insight-positive/negative connections, i.e. a positive/negative correlation between the ITB score and individual RSFCs between two brain regions. Insight-specific connections included motor-related regions whereas creative-common connections included a default mode network. Our results indicate that creative insight requires a coupling of multiple networks, such as the default mode, semantic and cerebral-cerebellum networks.

Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development.

PubMed

Palumbo, Maria Concetta; Zenoni, Sara; Fasoli, Marianna; Massonnet, Mélanie; Farina, Lorenzo; Castiglione, Filippo; Pezzotti, Mario; Paci, Paola

2014-12-01

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named "fight-club hubs" characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named "switch genes" was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops. © 2014 American Society of Plant Biologists. All rights reserved.

Integrated Network Analysis Identifies Fight-Club Nodes as a Class of Hubs Encompassing Key Putative Switch Genes That Induce Major Transcriptome Reprogramming during Grapevine Development[W][OPEN

PubMed Central

Palumbo, Maria Concetta; Zenoni, Sara; Fasoli, Marianna; Massonnet, Mélanie; Farina, Lorenzo; Castiglione, Filippo; Pezzotti, Mario; Paci, Paola

2014-01-01

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named “fight-club hubs” characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named “switch genes” was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops. PMID:25490918

Network neighborhood analysis with the multi-node topological overlap measure.

PubMed

Li, Ai; Horvath, Steve

2007-01-15

The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. Since the use of multiple nodes in the seed set may lead to more informative neighborhoods, it can be advantageous to define multi-node similarity measures. The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. Using four network applications and a simulated example, we provide empirical evidence that the resulting neighborhoods are biologically meaningful, e.g. we use neighborhood analysis to identify brain cancer related genes. An executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the webpage (http://www.genetics.ucla.edu/labs/horvath/MTOM/).

Agonistic reciprocity is associated with reduced male reproductive success within haremic social networks

PubMed Central

Solomon-Lane, Tessa K.; Pradhan, Devaleena S.; Willis, Madelyne C.; Grober, Matthew S.

2015-01-01

While individual variation in social behaviour is ubiquitous and causes social groups to differ in structure, how these structural differences affect fitness remains largely unknown. We used social network analysis of replicate bluebanded goby (Lythrypnus dalli) harems to identify the reproductive correlates of social network structure. In stable groups, we quantified agonistic behaviour, reproduction and steroid hormones, which can both affect and respond to social/reproductive cues. We identified distinct, optimal social structures associated with different reproductive measures. Male hatching success (HS) was negatively associated with agonistic reciprocity, a network structure that describes whether subordinates ‘reciprocated’ agonism received from dominants. Egg laying was associated with the individual network positions of the male and dominant female. Thus, males face a trade-off between promoting structures that facilitate egg laying versus HS. Whether this reproductive conflict is avoidable remains to be determined. We also identified different social and/or reproductive roles for 11-ketotestosterone, 17β-oestradiol and cortisol, suggesting that specific neuroendocrine mechanisms may underlie connections between network structure and fitness. This is one of the first investigations of the reproductive and neuroendocrine correlates of social behaviour and network structure in replicate, naturalistic social groups and supports network structure as an important target for natural selection. PMID:26156769

Identifying influential spreaders in complex networks through local effective spreading paths

NASA Astrophysics Data System (ADS)

Wang, Xiaojie; Zhang, Xue; Yi, Dongyun; Zhao, Chengli

2017-05-01

How to effectively identify a set of influential spreaders in complex networks is of great theoretical and practical value, which can help to inhibit the rapid spread of epidemics, promote the sales of products by word-of-mouth advertising, and so on. A naive strategy is to select the top ranked nodes as identified by some centrality indices, and other strategies are mainly based on greedy methods and heuristic methods. However, most of those approaches did not concern the connections between nodes. Usually, the distances between the selected spreaders are very close, leading to a serious overlapping of their influence. As a consequence, the global influence of the spreaders in networks will be greatly reduced, which largely restricts the performance of those methods. In this paper, a simple and efficient method is proposed to identify a set of discrete yet influential spreaders. By analyzing the spreading paths in the network, we present the concept of effective spreading paths and measure the influence of nodes via expectation calculation. The numerical analysis in undirected and directed networks all show that our proposed method outperforms many other centrality-based and heuristic benchmarks, especially in large-scale networks. Besides, experimental results on different spreading models and parameters demonstrates the stability and wide applicability of our method.

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

PubMed Central

Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works. PMID:26977400

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

PubMed

Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.

PubMed

Kaiser, Roselinde H; Andrews-Hanna, Jessica R; Wager, Tor D; Pizzagalli, Diego A

2015-06-01

Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. To investigate network dysfunction in MDD through a meta-analysis of rsFC studies. Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data. Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis. Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network. Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions. Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.

Identification of conserved drought stress responsive gene-network across tissues and developmental stages in rice.

PubMed

Smita, Shuchi; Katiyar, Amit; Pandey, Dev Mani; Chinnusamy, Viswanathan; Archak, Sunil; Bansal, Kailash Chander

2013-01-01

Identification of genes that are coexpressed across various tissues and environmental stresses is biologically interesting, since they may play coordinated role in similar biological processes. Genes with correlated expression patterns can be best identified by using coexpression network analysis of transcriptome data. In the present study, we analyzed the temporal-spatial coordination of gene expression in root, leaf and panicle of rice under drought stress and constructed network using WGCNA and Cytoscape. Total of 2199 differentially expressed genes (DEGs) were identified in at least three or more tissues, wherein 88 genes have coordinated expression profile among all the six tissues under drought stress. These 88 highly coordinated genes were further subjected to module identification in the coexpression network. Based on chief topological properties we identified 18 hub genes such as ABC transporter, ATP-binding protein, dehydrin, protein phosphatase 2C, LTPL153 - Protease inhibitor, phosphatidylethanolaminebinding protein, lactose permease-related, NADP-dependent malic enzyme, etc. Motif enrichment analysis showed the presence of ABRE cis-elements in the promoters of > 62% of the coordinately expressed genes. Our results suggest that drought stress mediated upregulated gene expression was coordinated through an ABA-dependent signaling pathway across tissues, at least for the subset of genes identified in this study, while down regulation appears to be regulated by tissue specific pathways in rice.

Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis.

PubMed

Jadhav, Ankush; Shanmugham, Buvaneswari; Rajendiran, Anjana; Pan, Archana

2014-10-01

Food and waterborne diseases are a growing concern in terms of human morbidity and mortality worldwide, even in the 21st century, emphasizing the need for new therapeutic interventions for these diseases. The current study aims at prioritizing broad-spectrum antibacterial targets, present in multiple food and waterborne bacterial pathogens, through a comparative genomics strategy coupled with a protein interaction network analysis. The pathways unique and common to all the pathogens under study (viz., methane metabolism, d-alanine metabolism, peptidoglycan biosynthesis, bacterial secretion system, two-component system, C5-branched dibasic acid metabolism), identified by comparative metabolic pathway analysis, were considered for the analysis. The proteins/enzymes involved in these pathways were prioritized following host non-homology analysis, essentiality analysis, gut flora non-homology analysis and protein interaction network analysis. The analyses revealed a set of promising broad-spectrum antibacterial targets, present in multiple food and waterborne pathogens, which are essential for bacterial survival, non-homologous to host and gut flora, and functionally important in the metabolic network. The identified broad-spectrum candidates, namely, integral membrane protein/virulence factor (MviN), preprotein translocase subunits SecB and SecG, carbon storage regulator (CsrA), and nitrogen regulatory protein P-II 1 (GlnB), contributed by the peptidoglycan pathway, bacterial secretion systems and two-component systems, were also found to be present in a wide range of other disease-causing bacteria. Cytoplasmic proteins SecG, CsrA and GlnB were considered as drug targets, while membrane proteins MviN and SecB were classified as vaccine targets. The identified broad-spectrum targets can aid in the design and development of antibacterial agents not only against food and waterborne pathogens but also against other pathogens. Copyright © 2014 Elsevier B.V. All rights reserved.

Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

PubMed

Yang, Jun; Hou, Ziming; Wang, Changjiang; Wang, Hao; Zhang, Hongbing

2018-04-23

Adamantinomatous craniopharyngioma (ACP) is an aggressive brain tumor that occurs predominantly in the pediatric population. Conventional diagnosis method and standard therapy cannot treat ACPs effectively. In this paper, we aimed to identify key genes for ACP early diagnosis and treatment. Datasets GSE94349 and GSE68015 were obtained from Gene Expression Omnibus database. Consensus clustering was applied to discover the gene clusters in the expression data of GSE94349 and functional enrichment analysis was performed on gene set in each cluster. The protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes, and hubs were selected. Support vector machine (SVM) model was built based on the signature genes identified from enrichment analysis and PPI network. Dataset GSE94349 was used for training and testing, and GSE68015 was used for validation. Besides, RT-qPCR analysis was performed to analyze the expression of signature genes in ACP samples compared with normal controls. Seven gene clusters were discovered in the differentially expressed genes identified from GSE94349 dataset. Enrichment analysis of each cluster identified 25 pathways that highly associated with ACP. PPI network was built and 46 hubs were determined. Twenty-five pathway-related genes that overlapped with the hubs in PPI network were used as signatures to establish the SVM diagnosis model for ACP. The prediction accuracy of SVM model for training, testing, and validation data were 94, 85, and 74%, respectively. The expression of CDH1, CCL2, ITGA2, COL8A1, COL6A2, and COL6A3 were significantly upregulated in ACP tumor samples, while CAMK2A, RIMS1, NEFL, SYT1, and STX1A were significantly downregulated, which were consistent with the differentially expressed gene analysis. SVM model is a promising classification tool for screening and early diagnosis of ACP. The ACP-related pathways and signature genes will advance our knowledge of ACP pathogenesis and benefit the therapy improvement.

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

An Empirical Typology of Social Networks and Its Association With Physical and Mental Health: A Study With Older Korean Immigrants

PubMed Central

Jang, Yuri; Lee, Beom S.; Ko, Jung Eun; Haley, William E.; Chiriboga, David A.

2015-01-01

Objectives. In the context of social convoy theory, the purposes of the study were (a) to identify an empirical typology of the social networks evident in older Korean immigrants and (b) to examine its association with self-rated health and depressive symptoms. Method. The sample consisted of 1,092 community-dwelling older Korean immigrants in Florida and New York. Latent class analyses were conducted to identify the optimal social network typology based on 8 indicators of interpersonal relationships and activities. Bivariate and multivariate analyses were conducted to examine how the identified social network typology was associated with self-rating of health and depressive symptoms. Results. Results from the latent class analysis identified 6 clusters as being most optimal, and they were named diverse, unmarried/diverse, married/coresidence, family focused, unmarried/restricted, and restricted. Memberships in the clusters of diverse and married/coresidence were significantly associated with more favorable ratings of health and lower levels of depressive symptoms. Discussion. Notably, no distinct network solely composed of friends was identified in the present sample of older immigrants; this may reflect the disruptions in social convoys caused by immigration. The findings of this study promote our understanding of the unique patterns of social connectedness in older immigrants. PMID:23887929

Co-occurrence correlations of heavy metals in sediments revealed using network analysis.

PubMed

Liu, Lili; Wang, Zhiping; Ju, Feng; Zhang, Tong

2015-01-01

In this study, the correlation-based study was used to identify the co-occurrence correlations among metals in marine sediment of Hong Kong, based on the long-term (from 1991 to 2011) temporal and spatial monitoring data. 14 stations out of the total 45 marine sediment monitoring stations were selected from three representative areas, including Deep Bay, Victoria Harbour and Mirs Bay. Firstly, Spearman's rank correlation-based network analysis was conducted as the first step to identify the co-occurrence correlations of metals from raw metadata, and then for further analysis using the normalized metadata. The correlations patterns obtained by network were consistent with those obtained by the other statistic normalization methods, including annual ratios, R-squared coefficient and Pearson correlation coefficient. Both Deep Bay and Victoria Harbour have been polluted by heavy metals, especially for Pb and Cu, which showed strong co-occurrence with other heavy metals (e.g. Cr, Ni, Zn and etc.) and little correlations with the reference parameters (Fe or Al). For Mirs Bay, which has better marine sediment quality compared with Deep Bay and Victoria Harbour, the co-occurrence patterns revealed by network analysis indicated that the metals in sediment dominantly followed the natural geography process. Besides the wide applications in biology, sociology and informatics, it is the first time to apply network analysis in the researches of environment pollutions. This study demonstrated its powerful application for revealing the co-occurrence correlations among heavy metals in marine sediments, which could be further applied for other pollutants in various environment systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

NASA Astrophysics Data System (ADS)

White, Forest M.; Wolf-Yadlin, Alejandro

2016-06-01

Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

From brain to earth and climate systems: small-world interaction networks or not?

PubMed

Bialonski, Stephan; Horstmann, Marie-Therese; Lehnertz, Klaus

2010-03-01

We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization.

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data

PubMed Central

Kümmel, Anne; Panke, Sven; Heinemann, Matthias

2006-01-01

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data. PMID:16788595

Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan

PubMed Central

Davison, Elizabeth N.; Turner, Benjamin O.; Miller, Michael B.; Carlson, Jean M.

2016-01-01

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism—hypergraph cardinality—we investigate individual variations in two separate, complementary data sets. The first data set (“multi-task”) consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set (“age-memory”), in which 95 individuals, aged 18–75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain. PMID:27880785

Multilayer motif analysis of brain networks

NASA Astrophysics Data System (ADS)

Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito

2017-04-01

In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

Personal and Impersonal Stimuli Differentially Engage Brain Networks during Moral Reasoning

ERIC Educational Resources Information Center

Xue, Shao-Wei; Wang, Yan; Tang, Yi-Yuan

2013-01-01

Moral decision making has recently attracted considerable attention as a core feature of all human endeavors. Previous functional magnetic resonance imaging studies about moral judgment have identified brain areas associated with cognitive or emotional engagement. Here, we applied graph theory-based network analysis of event-related potentials…

Simplified Techniques for Evaluation and Interpretation of Pavement Deflections for Network-level Analysis : Guide for Assessment of Pavement Structure Performance for PMS Applications

DOT National Transportation Integrated Search

2012-06-01

The objective of this study was to develop an approach for incorporating techniques to interpret and evaluate deflection : data for network-level pavement management system (PMS) applications. The first part of this research focused on : identifying ...

Large-Scale Analysis of Network Bistability for Human Cancers

PubMed Central

Shiraishi, Tetsuya; Matsuyama, Shinako; Kitano, Hiroaki

2010-01-01

Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers. PMID:20628618

Analysis of Gene Regulatory Networks of Maize in Response to Nitrogen.

PubMed

Jiang, Lu; Ball, Graham; Hodgman, Charlie; Coules, Anne; Zhao, Han; Lu, Chungui

2018-03-08

Nitrogen (N) fertilizer has a major influence on the yield and quality. Understanding and optimising the response of crop plants to nitrogen fertilizer usage is of central importance in enhancing food security and agricultural sustainability. In this study, the analysis of gene regulatory networks reveals multiple genes and biological processes in response to N. Two microarray studies have been used to infer components of the nitrogen-response network. Since they used different array technologies, a map linking the two probe sets to the maize B73 reference genome has been generated to allow comparison. Putative Arabidopsis homologues of maize genes were used to query the Biological General Repository for Interaction Datasets (BioGRID) network, which yielded the potential involvement of three transcription factors (TFs) (GLK5, MADS64 and bZIP108) and a Calcium-dependent protein kinase. An Artificial Neural Network was used to identify influential genes and retrieved bZIP108 and WRKY36 as significant TFs in both microarray studies, along with genes for Asparagine Synthetase, a dual-specific protein kinase and a protein phosphatase. The output from one study also suggested roles for microRNA (miRNA) 399b and Nin-like Protein 15 (NLP15). Co-expression-network analysis of TFs with closely related profiles to known Nitrate-responsive genes identified GLK5, GLK8 and NLP15 as candidate regulators of genes repressed under low Nitrogen conditions, while bZIP108 might play a role in gene activation.

Network methods to support user involvement in qualitative data analyses: an introduction to Participatory Theme Elicitation.

PubMed

Best, Paul; Badham, Jennifer; Corepal, Rekesh; O'Neill, Roisin F; Tully, Mark A; Kee, Frank; Hunter, Ruth F

2017-11-23

While Patient and Public Involvement (PPI) is encouraged throughout the research process, engagement is typically limited to intervention design and post-analysis stages. There are few approaches to participatory data analyses within complex health interventions. Using qualitative data from a feasibility randomised controlled trial (RCT), this proof-of-concept study tests the value of a new approach to participatory data analysis called Participatory Theme Elicitation (PTE). Forty excerpts were given to eight members of a youth advisory PPI panel to sort into piles based on their perception of related thematic content. Using algorithms to detect communities in networks, excerpts were then assigned to a thematic cluster that combined the panel members' perspectives. Network analysis techniques were also used to identify key excerpts in each grouping that were then further explored qualitatively. While PTE analysis was, for the most part, consistent with the researcher-led analysis, young people also identified new emerging thematic content. PTE appears promising for encouraging user led identification of themes arising from qualitative data collected during complex interventions. Further work is required to validate and extend this method. ClinicalTrials.gov, ID: NCT02455986 . Retrospectively Registered on 21 May 2015.

An adaptive neural swarm approach for intrusion defense in ad hoc networks

NASA Astrophysics Data System (ADS)

Cannady, James

2011-06-01

Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.

A Systems Biology Framework Identifies Molecular Underpinnings of Coronary Heart Disease

PubMed Central

Huan, Tianxiao; Zhang, Bin; Wang, Zhi; Joehanes, Roby; Zhu, Jun; Johnson, Andrew D.; Ying, Saixia; Munson, Peter J.; Raghavachari, Nalini; Wang, Richard; Liu, Poching; Courchesne, Paul; Hwang, Shih-Jen; Assimes, Themistocles L.; McPherson, Ruth; Samani, Nilesh J.; Schunkert, Heribert; Meng, Qingying; Suver, Christine; O'Donnell, Christopher J.; Derry, Jonathan; Yang, Xia; Levy, Daniel

2013-01-01

Objective Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD. Approach and Results We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. 24 coexpression modules were identified including one case-specific and one control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with altered gene expression associated SNPs (eSNPs) and with results of GWAS of CHD and its risk factors, the control-specific DM was implicated as CHD-causal based on its significant enrichment for both CHD and lipid eSNPs. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver (KD) genes. Multi-tissue KDs (SPIB and TNFRSF13C) and tissue-specific KDs (e.g. EBF1) were identified. Conclusions Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk. PMID:23539213

Controllability of flow-conservation networks

NASA Astrophysics Data System (ADS)

Zhao, Chen; Zeng, An; Jiang, Rui; Yuan, Zhengzhong; Wang, Wen-Xu

2017-07-01

The ultimate goal of exploring complex networks is to control them. As such, controllability of complex networks has been intensively investigated. Despite recent advances in studying the impact of a network's topology on its controllability, a comprehensive understanding of the synergistic impact of network topology and dynamics on controllability is still lacking. Here, we explore the controllability of flow-conservation networks, trying to identify the minimal number of driver nodes that can guide the network to any desirable state. We develop a method to analyze the controllability on flow-conservation networks based on exact controllability theory, transforming the original analysis on adjacency matrix to Laplacian matrix. With this framework, we systematically investigate the impact of some key factors of networks, including link density, link directionality, and link polarity, on the controllability of these networks. We also obtain the analytical equations by investigating the network's structural properties approximatively and design the efficient tools. Finally, we consider some real networks with flow dynamics, finding that their controllability is significantly different from that predicted by only considering the topology. These findings deepen our understanding of network controllability with flow-conservation dynamics and provide a general framework to incorporate real dynamics in the analysis of network controllability.

Mapping Multiplex Hubs in Human Functional Brain Networks

PubMed Central

De Domenico, Manlio; Sasai, Shuntaro; Arenas, Alex

2016-01-01

Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches. PMID:27471443

Supporting Research and Development of Security Technologies through Network and Security Data Collection

DTIC Science & Technology

Research and development targeted at identifying and mitigating Internet security threats require current network data. To fulfill this need... researchers working for the Center for Applied Internet Data Analysis (CAIDA), a program at the San Diego Supercomputer Center (SDSC) which is based at the...vetted network and security researchers using the PREDICT/IMPACT portal and legal framework. We have also contributed to community building efforts that

Using egocentric analysis to investigate professional networks and productivity of graduate students and faculty in life sciences in Japan, Singapore, and Taiwan.

PubMed

Hara, Noriko; Chen, Hui; Ynalvez, Marcus Antonius

2017-01-01

Prior studies showed that scientists' professional networks contribute to research productivity, but little work has examined what factors predict the formation of professional networks. This study sought to 1) examine what factors predict the formation of international ties between faculty and graduate students and 2) identify how these international ties would affect publication productivity in three East Asian countries. Face-to-face surveys and in-depth semi-structured interviews were conducted with a sample of faculty and doctoral students in life sciences at 10 research institutions in Japan, Singapore, and Taiwan. Our final sample consisted of 290 respondents (84 faculty and 206 doctoral students) and 1,435 network members. We used egocentric social network analysis to examine the structure of international ties and how they relate to research productivity. Our findings suggest that overseas graduate training can be a key factor in graduate students' development of international ties in these countries. Those with a higher proportion of international ties in their professional networks were likely to have published more papers and written more manuscripts. For faculty, international ties did not affect the number of manuscripts written or of papers published, but did correlate with an increase in publishing in top journals. The networks we examined were identified by asking study participants with whom they discuss their research. Because the relationships may not appear in explicit co-authorship networks, these networks were not officially recorded elsewhere. This study sheds light on the relationships of these invisible support networks to researcher productivity.

Use of social network analysis in maternity care to identify the profession most suited for case manager role.

PubMed

Groenen, Carola J M; van Duijnhoven, Noortje T L; Faber, Marjan J; Koetsenruijter, Jan; Kremer, Jan A M; Vandenbussche, Frank P H A

2017-02-01

To improve Dutch maternity care, professionals start working in interdisciplinary patient-centred networks, which includes the patients as a member. The introduction of the case manager is expected to work positively on both the individual and the network level. However, case management is new in Dutch maternity care. The present study aims to define the profession that would be most suitable to fulfil the role of case manager. The maternal care network in the Nijmegen region was determined by using Social Network Analysis (SNA). SNA is a quantitative methodology that measures and analyses patient-related connections between different professionals working in a network. To identify the case manager we focused on the position, reach, and connections in the network of the maternal care professionals. Maternity healthcare professionals in a single region of the Netherlands with an average of 4,500 births/year. The participants were 214 individual healthcare workers from eight different professions. The total network showed 3948 connections between 214 maternity healthcare professionals with a density of 0.08. Each profession had some central individuals in the network. The 52 community-based midwives were responsible for 51% of all measured connections. The youth health doctors and nurses were mostly situated on the periphery and less connected. The betweenness centrality had the highest score in obstetricians and community-based midwives. Only the community-based midwives had connections with all other groups of professions. Almost all professionals in the network could reach other professionals in two steps. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using egocentric analysis to investigate professional networks and productivity of graduate students and faculty in life sciences in Japan, Singapore, and Taiwan

PubMed Central

Chen, Hui; Ynalvez, Marcus Antonius

2017-01-01

Prior studies showed that scientists’ professional networks contribute to research productivity, but little work has examined what factors predict the formation of professional networks. This study sought to 1) examine what factors predict the formation of international ties between faculty and graduate students and 2) identify how these international ties would affect publication productivity in three East Asian countries. Face-to-face surveys and in-depth semi-structured interviews were conducted with a sample of faculty and doctoral students in life sciences at 10 research institutions in Japan, Singapore, and Taiwan. Our final sample consisted of 290 respondents (84 faculty and 206 doctoral students) and 1,435 network members. We used egocentric social network analysis to examine the structure of international ties and how they relate to research productivity. Our findings suggest that overseas graduate training can be a key factor in graduate students’ development of international ties in these countries. Those with a higher proportion of international ties in their professional networks were likely to have published more papers and written more manuscripts. For faculty, international ties did not affect the number of manuscripts written or of papers published, but did correlate with an increase in publishing in top journals. The networks we examined were identified by asking study participants with whom they discuss their research. Because the relationships may not appear in explicit co-authorship networks, these networks were not officially recorded elsewhere. This study sheds light on the relationships of these invisible support networks to researcher productivity. PMID:29045500

The Orphan Disease Networks

PubMed Central

Zhang, Minlu; Zhu, Cheng; Jacomy, Alexis; Lu, Long J.; Jegga, Anil G.

2011-01-01

The low prevalence rate of orphan diseases (OD) requires special combined efforts to improve diagnosis, prevention, and discovery of novel therapeutic strategies. To identify and investigate relationships based on shared genes or shared functional features, we have conducted a bioinformatic-based global analysis of all orphan diseases with known disease-causing mutant genes. Starting with a bipartite network of known OD and OD-causing mutant genes and using the human protein interactome, we first construct and topologically analyze three networks: the orphan disease network, the orphan disease-causing mutant gene network, and the orphan disease-causing mutant gene interactome. Our results demonstrate that in contrast to the common disease-causing mutant genes that are predominantly nonessential, a majority of orphan disease-causing mutant genes are essential. In confirmation of this finding, we found that OD-causing mutant genes are topologically important in the protein interactome and are ubiquitously expressed. Additionally, functional enrichment analysis of those genes in which mutations cause ODs shows that a majority result in premature death or are lethal in the orthologous mouse gene knockout models. To address the limitations of traditional gene-based disease networks, we also construct and analyze OD networks on the basis of shared enriched features (biological processes, cellular components, pathways, phenotypes, and literature citations). Analyzing these functionally-linked OD networks, we identified several additional OD-OD relations that are both phenotypically similar and phenotypically diverse. Surprisingly, we observed that the wiring of the gene-based and other feature-based OD networks are largely different; this suggests that the relationship between ODs cannot be fully captured by the gene-based network alone. PMID:21664998

Modelling the structure of a ceRNA-theoretical, bipartite microRNA-mRNA interaction network regulating intestinal epithelial cellular pathways using R programming.

PubMed

Robinson, J M; Henderson, W A

2018-01-12

We report a method using functional-molecular databases and network modelling to identify hypothetical mRNA-miRNA interaction networks regulating intestinal epithelial barrier function. The model forms a data-analysis component of our cell culture experiments, which produce RNA expression data from Nanostring Technologies nCounter ® system. The epithelial tight-junction (TJ) and actin cytoskeleton interact as molecular components of the intestinal epithelial barrier. Upstream regulation of TJ-cytoskeleton interaction is effected by the Rac/Rock/Rho signaling pathway and other associated pathways which may be activated or suppressed by extracellular signaling from growth factors, hormones, and immune receptors. Pathway activations affect epithelial homeostasis, contributing to degradation of the epithelial barrier associated with osmotic dysregulation, inflammation, and tumor development. The complexity underlying miRNA-mRNA interaction networks represents a roadblock for prediction and validation of competing-endogenous RNA network function. We developed a network model to identify hypothetical co-regulatory motifs in a miRNA-mRNA interaction network related to epithelial function. A mRNA-miRNA interaction list was generated using KEGG and miRWalk2.0 databases. R-code was developed to quantify and visualize inherent network structures. We identified a sub-network with a high number of shared, targeting miRNAs, of genes associated with cellular proliferation and cancer, including c-MYC and Cyclin D.

Towards understanding the behavior of physical systems using information theory

NASA Astrophysics Data System (ADS)

Quax, Rick; Apolloni, Andrea; Sloot, Peter M. A.

2013-09-01

One of the goals of complex network analysis is to identify the most influential nodes, i.e., the nodes that dictate the dynamics of other nodes. In the case of autonomous systems or transportation networks, highly connected hubs play a preeminent role in diffusing the flow of information and viruses; in contrast, in language evolution most linguistic norms come from the peripheral nodes who have only few contacts. Clearly a topological analysis of the interactions alone is not sufficient to identify the nodes that drive the state of the network. Here we show how information theory can be used to quantify how the dynamics of individual nodes propagate through a system. We interpret the state of a node as a storage of information about the state of other nodes, which is quantified in terms of Shannon information. This information is transferred through interactions and lost due to noise, and we calculate how far it can travel through a network. We apply this concept to a model of opinion formation in a complex social network to calculate the impact of each node by measuring how long its opinion is remembered by the network. Counter-intuitively we find that the dynamics of opinions are not determined by the hubs or peripheral nodes, but rather by nodes with an intermediate connectivity.

Examining ISIS Support and Opposition Networks on Twitter

DTIC Science & Technology

2016-01-01

interactive communities of Twitter users , lexical analysis that can identify key themes and content for large data sets, and social network analysis...Twitter data, we lexically analyzed the content and key themes of users who mostly employ Daesh versus those who mostly use Islamic State in their tweets...As predicted, we found that frequent users of Daesh had content that was highly critical of ISIS, with users using such terms as Terrorist Daesh

Resolution of ranking hierarchies in directed networks.

PubMed

Letizia, Elisa; Barucca, Paolo; Lillo, Fabrizio

2018-01-01

Identifying hierarchies and rankings of nodes in directed graphs is fundamental in many applications such as social network analysis, biology, economics, and finance. A recently proposed method identifies the hierarchy by finding the ordered partition of nodes which minimises a score function, termed agony. This function penalises the links violating the hierarchy in a way depending on the strength of the violation. To investigate the resolution of ranking hierarchies we introduce an ensemble of random graphs, the Ranked Stochastic Block Model. We find that agony may fail to identify hierarchies when the structure is not strong enough and the size of the classes is small with respect to the whole network. We analytically characterise the resolution threshold and we show that an iterated version of agony can partly overcome this resolution limit.

Resolution of ranking hierarchies in directed networks

PubMed Central

Barucca, Paolo; Lillo, Fabrizio

2018-01-01

Identifying hierarchies and rankings of nodes in directed graphs is fundamental in many applications such as social network analysis, biology, economics, and finance. A recently proposed method identifies the hierarchy by finding the ordered partition of nodes which minimises a score function, termed agony. This function penalises the links violating the hierarchy in a way depending on the strength of the violation. To investigate the resolution of ranking hierarchies we introduce an ensemble of random graphs, the Ranked Stochastic Block Model. We find that agony may fail to identify hierarchies when the structure is not strong enough and the size of the classes is small with respect to the whole network. We analytically characterise the resolution threshold and we show that an iterated version of agony can partly overcome this resolution limit. PMID:29394278

Neural correlates of the natural observation of an emotionally loaded video

PubMed Central

Gonzalez-Santos, Leopoldo

2018-01-01

Studies based on a paradigm of free or natural viewing have revealed characteristics that allow us to know how the brain processes stimuli within a natural environment. This method has been little used to study brain function. With a connectivity approach, we examine the processing of emotions using an exploratory method to analyze functional magnetic resonance imaging (fMRI) data. This research describes our approach to modeling stress paradigms suitable for neuroimaging environments. We showed a short film (4.54 minutes) with high negative emotional valence and high arousal content to 24 healthy male subjects (36.42 years old; SD = 12.14) during fMRI. Independent component analysis (ICA) was used to identify networks based on spatial statistical independence. Through this analysis we identified the sensorimotor system and its influence on the dorsal attention and default-mode networks, which in turn have reciprocal activity and modulate networks described as emotional. PMID:29883494

Tracking cohesive subgroups over time in inferred social networks

NASA Astrophysics Data System (ADS)

Chin, Alvin; Chignell, Mark; Wang, Hao

2010-04-01

As a first step in the development of community trackers for large-scale online interaction, this paper shows how cohesive subgroup analysis using the Social Cohesion Analysis of Networks (SCAN; Chin and Chignell 2008) and Data-Intensive Socially Similar Evolving Community Tracker (DISSECT; Chin and Chignell 2010) methods can be applied to the problem of identifying cohesive subgroups and tracking them over time. Three case studies are reported, and the findings are used to evaluate how well the SCAN and DISSECT methods work for different types of data. In the largest of the case studies, variations in temporal cohesiveness are identified across a set of subgroups extracted from the inferred social network. Further modifications to the DISSECT methodology are suggested based on the results obtained. The paper concludes with recommendations concerning further research that would be beneficial in addressing the community tracking problem for online data.

Identification of Major Signaling Pathways in Prion Disease Progression Using Network Analysis

PubMed Central

Newaz, Khalique; Sriram, K.; Bera, Debajyoti

2015-01-01

Prion diseases are transmissible neurodegenerative diseases that arise due to conformational change of normal, cellular prion protein (PrPC) to protease-resistant isofrom (rPrPSc). Deposition of misfolded PrpSc proteins leads to an alteration of many signaling pathways that includes immunological and apoptotic pathways. As a result, this culminates in the dysfunction and death of neuronal cells. Earlier works on transcriptomic studies have revealed some affected pathways, but it is not clear which is (are) the prime network pathway(s) that change during the disease progression and how these pathways are involved in crosstalks with each other from the time of incubation to clinical death. We perform network analysis on large-scale transcriptomic data of differentially expressed genes obtained from whole brain in six different mouse strain-prion strain combination models to determine the pathways involved in prion diseases, and to understand the role of crosstalks in disease propagation. We employ a notion of differential network centrality measures on protein interaction networks to identify the potential biological pathways involved. We also propose a crosstalk ranking method based on dynamic protein interaction networks to identify the core network elements involved in crosstalk with different pathways. We identify 148 DEGs (differentially expressed genes) potentially related to the prion disease progression. Functional association of the identified genes implicates a strong involvement of immunological pathways. We extract a bow-tie structure that is potentially dysregulated in prion disease. We also propose an ODE model for the bow-tie network. Predictions related to diseased condition suggests the downregulation of the core signaling elements (PI3Ks and AKTs) of the bow-tie network. In this work, we show using transcriptomic data that the neuronal dysfunction in prion disease is strongly related to the immunological pathways. We conclude that these immunological pathways occupy influential positions in the PFNs (protein functional networks) that are related to prion disease. Importantly, this functional network involvement is prevalent in all the five different mouse strain-prion strain combinations that we studied. We also conclude that the dysregulation of the core elements of the bow-tie structure, which belongs to PI3K-Akt signaling pathway, leads to dysregulation of the downstream components corresponding to other biological pathways. PMID:26646948

SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity.

PubMed

Lee, Kangjoo; Lina, Jean-Marc; Gotman, Jean; Grova, Christophe

2016-07-01

Functional hubs are defined as the specific brain regions with dense connections to other regions in a functional brain network. Among them, connector hubs are of great interests, as they are assumed to promote global and hierarchical communications between functionally specialized networks. Damage to connector hubs may have a more crucial effect on the system than does damage to other hubs. Hubs in graph theory are often identified from a correlation matrix, and classified as connector hubs when the hubs are more connected to regions in other networks than within the networks to which they belong. However, the identification of hubs from functional data is more complex than that from structural data, notably because of the inherent problem of multicollinearity between temporal dynamics within a functional network. In this context, we developed and validated a method to reliably identify connectors and corresponding overlapping network structure from resting-state fMRI. This new method is actually handling the multicollinearity issue, since it does not rely on counting the number of connections from a thresholded correlation matrix. The novelty of the proposed method is that besides counting the number of networks involved in each voxel, it allows us to identify which networks are actually involved in each voxel, using a data-driven sparse general linear model in order to identify brain regions involved in more than one network. Moreover, we added a bootstrap resampling strategy to assess statistically the reproducibility of our results at the single subject level. The unified framework is called SPARK, i.e. SParsity-based Analysis of Reliable k-hubness, where k-hubness denotes the number of networks overlapping in each voxel. The accuracy and robustness of SPARK were evaluated using two dimensional box simulations and realistic simulations that examined detection of artificial hubs generated on real data. Then, test/retest reliability of the method was assessed using the 1000 Functional Connectome Project database, which includes data obtained from 25 healthy subjects at three different occasions with long and short intervals between sessions. We demonstrated that SPARK provides an accurate and reliable estimation of k-hubness, suggesting a promising tool for understanding hub organization in resting-state fMRI. Copyright © 2016 Elsevier Inc. All rights reserved.

Sparse representation of whole-brain fMRI signals for identification of functional networks.

PubMed

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

2015-02-01

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

Patterns in PARTNERing across Public Health Collaboratives.

PubMed

Bevc, Christine A; Retrum, Jessica H; Varda, Danielle M

2015-10-05

Inter-organizational networks represent one of the most promising practice-based approaches in public health as a way to attain resources, share knowledge, and, in turn, improve population health outcomes. However, the interdependencies and effectiveness related to the structure, management, and costs of these networks represents a critical item to be addressed. The objective of this research is to identify and determine the extent to which potential partnering patterns influence the structure of collaborative networks. This study examines data collected by PARTNER, specifically public health networks (n = 162), to better understand the structured relationships and interactions among public health organizations and their partners, in relation to collaborative activities. Combined with descriptive analysis, we focus on the composition of public health collaboratives in a series of Exponential Random Graph (ERG) models to examine the partnerships between different organization types to identify the attribute-based effects promoting the formation of network ties within and across collaboratives. We found high variation within and between these collaboratives including composition, diversity, and interactions. The findings of this research suggest common and frequent types of partnerships, as well as opportunities to develop new collaborations. The result of this analysis offer additional evidence to inform and strengthen public health practice partnerships.

Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

PubMed Central

Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Tabatabaei, Seyyed Mohammad; Namaki, Saeed

2016-01-01

Background The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease. Methods Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression. Results The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways. Discussion This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets. PMID:28028462

Comprehensive analysis of differentially expressed profiles of lncRNAs and construction of miR-133b mediated ceRNA network in colorectal cancer.

PubMed

Wu, Hao; Wu, Runliu; Chen, Miao; Li, Daojiang; Dai, Jing; Zhang, Yi; Gao, Kai; Yu, Jun; Hu, Gui; Guo, Yihang; Lin, Changwei; Li, Xiaorong

2017-03-28

Growing evidence suggests that long non-coding RNAs (lncRNAs) play a key role in tumorigenesis. However, the mechanism remains largely unknown. Thousands of significantly dysregulated lncRNAs and mRNAs were identified by microarray. Furthermore, a miR-133b-meditated lncRNA-mRNA ceRNA network was revealed, a subset of which was validated in 14 paired CRC patient tumor/non-tumor samples. Gene set enrichment analysis (GSEA) results demonstrated that lncRNAs ENST00000520055 and ENST00000535511 shared KEGG pathways with miR-133b target genes. We used microarrays to survey the lncRNA and mRNA expression profiles of colorectal cancer and para-cancer tissues. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed to explore the functions of the significantly dysregulated genes. An innovate method was employed that combined analyses of two microarray data sets to construct a miR-133b-mediated lncRNA-mRNA competing endogenous RNAs (ceRNA) network. Quantitative RT-PCR analysis was used to validate part of this network. GSEA was used to predict the potential functions of these lncRNAs. This study identifies and validates a new method to investigate the miR-133b-mediated lncRNA-mRNA ceRNA network and lays the foundation for future investigation into the role of lncRNAs in colorectal cancer.

Psychophysiological whole-brain network clustering based on connectivity dynamics analysis in naturalistic conditions.

PubMed

Raz, Gal; Shpigelman, Lavi; Jacob, Yael; Gonen, Tal; Benjamini, Yoav; Hendler, Talma

2016-12-01

We introduce a novel method for delineating context-dependent functional brain networks whose connectivity dynamics are synchronized with the occurrence of a specific psychophysiological process of interest. In this method of context-related network dynamics analysis (CRNDA), a continuous psychophysiological index serves as a reference for clustering the whole-brain into functional networks. We applied CRNDA to fMRI data recorded during the viewing of a sadness-inducing film clip. The method reliably demarcated networks in which temporal patterns of connectivity related to the time series of reported emotional intensity. Our work successfully replicated the link between network connectivity and emotion rating in an independent sample group for seven of the networks. The demarcated networks have clear common functional denominators. Three of these networks overlap with distinct empathy-related networks, previously identified in distinct sets of studies. The other networks are related to sensorimotor processing, language, attention, and working memory. The results indicate that CRNDA, a data-driven method for network clustering that is sensitive to transient connectivity patterns, can productively and reliably demarcate networks that follow psychologically meaningful processes. Hum Brain Mapp 37:4654-4672, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Three-dimensional evidence network plot system: covariate imbalances and effects in network meta-analysis explored using a new software tool.

PubMed

Batson, Sarah; Score, Robert; Sutton, Alex J

2017-06-01

The aim of the study was to develop the three-dimensional (3D) evidence network plot system-a novel web-based interactive 3D tool to facilitate the visualization and exploration of covariate distributions and imbalances across evidence networks for network meta-analysis (NMA). We developed the 3D evidence network plot system within an AngularJS environment using a third party JavaScript library (Three.js) to create the 3D element of the application. Data used to enable the creation of the 3D element for a particular topic are inputted via a Microsoft Excel template spreadsheet that has been specifically formatted to hold these data. We display and discuss the findings of applying the tool to two NMA examples considering multiple covariates. These two examples have been previously identified as having potentially important covariate effects and allow us to document the various features of the tool while illustrating how it can be used. The 3D evidence network plot system provides an immediate, intuitive, and accessible way to assess the similarity and differences between the values of covariates for individual studies within and between each treatment contrast in an evidence network. In this way, differences between the studies, which may invalidate the usual assumptions of an NMA, can be identified for further scrutiny. Hence, the tool facilitates NMA feasibility/validity assessments and aids in the interpretation of NMA results. The 3D evidence network plot system is the first tool designed specifically to visualize covariate distributions and imbalances across evidence networks in 3D. This will be of primary interest to systematic review and meta-analysis researchers and, more generally, those assessing the validity and robustness of an NMA to inform reimbursement decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

Exercise-associated DNA methylation change in skeletal muscle and the importance of imprinted genes: a bioinformatics meta-analysis.

PubMed

Brown, William M

2015-12-01

Epigenetics is the study of processes--beyond DNA sequence alteration--producing heritable characteristics. For example, DNA methylation modifies gene expression without altering the nucleotide sequence. A well-studied DNA methylation-based phenomenon is genomic imprinting (ie, genotype-independent parent-of-origin effects). We aimed to elucidate: (1) the effect of exercise on DNA methylation and (2) the role of imprinted genes in skeletal muscle gene networks (ie, gene group functional profiling analyses). Gene ontology (ie, gene product elucidation)/meta-analysis. 26 skeletal muscle and 86 imprinted genes were subjected to g:Profiler ontology analysis. Meta-analysis assessed exercise-associated DNA methylation change. g:Profiler found four muscle gene networks with imprinted loci. Meta-analysis identified 16 articles (387 genes/1580 individuals) associated with exercise. Age, method, sample size, sex and tissue variation could elevate effect size bias. Only skeletal muscle gene networks including imprinted genes were reported. Exercise-associated effect sizes were calculated by gene. Age, method, sample size, sex and tissue variation were moderators. Six imprinted loci (RB1, MEG3, UBE3A, PLAGL1, SGCE, INS) were important for muscle gene networks, while meta-analysis uncovered five exercise-associated imprinted loci (KCNQ1, MEG3, GRB10, L3MBTL1, PLAGL1). DNA methylation decreased with exercise (60% of loci). Exercise-associated DNA methylation change was stronger among older people (ie, age accounted for 30% of the variation). Among older people, genes exhibiting DNA methylation decreases were part of a microRNA-regulated gene network functioning to suppress cancer. Imprinted genes were identified in skeletal muscle gene networks and exercise-associated DNA methylation change. Exercise-associated DNA methylation modification could rewind the 'epigenetic clock' as we age. CRD42014009800. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

How to Identify Success Among Networks That Promote Active Living

PubMed Central

Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-01-01

Objectives. We evaluated organization- and network-level factors that influence organizations’ perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. Methods. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. Results. A total of 53 of 59 “whole networks” met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%–100%). Occupying a leadership position (P < .01), the amount of time with the network (P < .05), and support from community leaders (P < .05) emerged as correlates of perceived success. Conclusions. Organizations’ perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities. PMID:26378863

Integration opportunities for HIV and family planning services in Addis Ababa, Ethiopia: an organizational network analysis

PubMed Central

2014-01-01

Background Public health resources are often deployed in developing countries by foreign governments, national governments, civil society and the private health clinics, but seldom in ways that are coordinated within a particular community or population. The lack of coordination results in inefficiencies and suboptimal results. Organizational network analysis can reveal how organizations interact with each other and provide insights into means of realizing better public health results from the resources already deployed. Our objective in this study was to identify the missed opportunities for the integration of HIV care and family planning services and to inform future network strengthening. Methods In two sub-cities of Addis Ababa, we identified each organization providing either HIV care or family planning services. We interviewed representatives of each of them about exchanges of clients with each of the others. With network analysis, we identified network characteristics in each sub-city network, such as referral density and centrality; and gaps in the referral patterns. The results were shared with representatives from the organizations. Results The two networks were of similar size (25 and 26 organizations) and had referral densities of 0.115 and 0.155 out of a possible range from 0 (none) to 1.0 (all possible connections). Two organizations in one sub-city did not refer HIV clients to a family planning organization. One organization in one sub-city and seven in the other offered few HIV services and did not refer clients to any other HIV service provider. Representatives from the networks confirmed the results reflected their experience and expressed an interest in establishing more links between organizations. Conclusions Because of organizations not working together, women in the two sub-cities were at risk of not receiving needed family planning or HIV care services. Facilitating referrals among a few organizations that are most often working in isolation could remediate the problem, but the overall referral densities suggests that improved connections throughout might benefit conditions in addition to HIV and family planning that need service integration. PMID:24438522

Integration opportunities for HIV and family planning services in Addis Ababa, Ethiopia: an organizational network analysis.

PubMed

Thomas, James C; Reynolds, Heidi; Bevc, Christine; Tsegaye, Ademe

2014-01-18

Public health resources are often deployed in developing countries by foreign governments, national governments, civil society and the private health clinics, but seldom in ways that are coordinated within a particular community or population. The lack of coordination results in inefficiencies and suboptimal results. Organizational network analysis can reveal how organizations interact with each other and provide insights into means of realizing better public health results from the resources already deployed. Our objective in this study was to identify the missed opportunities for the integration of HIV care and family planning services and to inform future network strengthening. In two sub-cities of Addis Ababa, we identified each organization providing either HIV care or family planning services. We interviewed representatives of each of them about exchanges of clients with each of the others. With network analysis, we identified network characteristics in each sub-city network, such as referral density and centrality; and gaps in the referral patterns. The results were shared with representatives from the organizations. The two networks were of similar size (25 and 26 organizations) and had referral densities of 0.115 and 0.155 out of a possible range from 0 (none) to 1.0 (all possible connections). Two organizations in one sub-city did not refer HIV clients to a family planning organization. One organization in one sub-city and seven in the other offered few HIV services and did not refer clients to any other HIV service provider. Representatives from the networks confirmed the results reflected their experience and expressed an interest in establishing more links between organizations. Because of organizations not working together, women in the two sub-cities were at risk of not receiving needed family planning or HIV care services. Facilitating referrals among a few organizations that are most often working in isolation could remediate the problem, but the overall referral densities suggests that improved connections throughout might benefit conditions in addition to HIV and family planning that need service integration.

Identification of tipping elements of the Indian Summer Monsoon using climate network approach

NASA Astrophysics Data System (ADS)

Stolbova, Veronika; Surovyatkina, Elena; Kurths, Jurgen

2015-04-01

Spatial and temporal variability of the rainfall is a vital question for more than one billion of people inhabiting the Indian subcontinent. Indian Summer Monsoon (ISM) rainfall is crucial for India's economy, social welfare, and environment and large efforts are being put into predicting the Indian Summer Monsoon. For predictability of the ISM, it is crucial to identify tipping elements - regions over the Indian subcontinent which play a key role in the spatial organization of the Indian monsoon system. Here, we use climate network approach for identification of such tipping elements of the ISM. First, we build climate networks of the extreme rainfall, surface air temperature and pressure over the Indian subcontinent for pre-monsoon, monsoon and post-monsoon seasons. We construct network of extreme rainfall event using observational satellite data from 1998 to 2012 from the Tropical Rainfall Measuring Mission (TRMM 3B42V7) and reanalysis gridded daily rainfall data for a time period of 57 years (1951-2007) (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE). For the network of surface air temperature and pressure fields, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). Second, we filter out data by coarse-graining the network through network measures, and identify tipping regions of the ISM. Finally, we compare obtained results of the network analysis with surface wind fields and show that occurrence of the tipping elements is mostly caused by monsoonal wind circulation, migration of the Intertropical Convergence Zone (ITCZ) and Westerlies. We conclude that climate network approach enables to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to identify tipping regions of the ISM. Obtained tipping elements deserve a special attention for the meteorologists and can be used as markers of the ISM variability.

Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case-control validation study.

PubMed

Li, Yiping; Li, Yanhong; Bai, Zhenjiang; Pan, Jian; Wang, Jian; Fang, Fang

2017-12-13

Sepsis represents a complex disease with the dysregulated inflammatory response and high mortality rate. The goal of this study was to identify potential transcriptomic markers in developing pediatric sepsis by a co-expression module analysis of the transcriptomic dataset. Using the R software and Bioconductor packages, we performed a weighted gene co-expression network analysis to identify co-expression modules significantly associated with pediatric sepsis. Functional interpretation (gene ontology and pathway analysis) and enrichment analysis with known transcription factors and microRNAs of the identified candidate modules were then performed. In modules significantly associated with sepsis, the intramodular analysis was further performed and "hub genes" were identified and validated by quantitative real-time PCR (qPCR) in this study. 15 co-expression modules in total were detected, and four modules ("midnight blue", "cyan", "brown", and "tan") were most significantly associated with pediatric sepsis and suggested as potential sepsis-associated modules. Gene ontology analysis and pathway analysis revealed that these four modules strongly associated with immune response. Three of the four sepsis-associated modules were also enriched with known transcription factors (false discovery rate-adjusted P < 0.05). Hub genes were identified in each of the four modules. Four of the identified hub genes (MYB proto-oncogene like 1, killer cell lectin like receptor G1, stomatin, and membrane spanning 4-domains A4A) were further validated to be differentially expressed between septic children and controls by qPCR. Four pediatric sepsis-associated co-expression modules were identified in this study. qPCR results suggest that hub genes in these modules are potential transcriptomic markers for pediatric sepsis diagnosis. These results provide novel insights into the pathogenesis of pediatric sepsis and promote the generation of diagnostic gene sets.

"Us and them": a social network analysis of physicians' professional networks and their attitudes towards EBM.

PubMed

Mascia, Daniele; Cicchetti, Americo; Damiani, Gianfranco

2013-10-22

Extant research suggests that there is a strong social component to Evidence-Based Medicine (EBM) adoption since professional networks amongst physicians are strongly associated with their attitudes towards EBM. Despite this evidence, it is still unknown whether individual attitudes to use scientific evidence in clinical decision-making influence the position that physicians hold in their professional network. This paper explores how physicians' attitudes towards EBM is related to the network position they occupy within healthcare organizations. Data pertain to a sample of Italian physicians, whose professional network relationships, demographics and work-profile characteristics were collected. A social network analysis was performed to capture the structural importance of physicians in the collaboration network by the means of a core-periphery analysis and the computation of network centrality indicators. Then, regression analysis was used to test the association between the network position of individual clinicians and their attitudes towards EBM. Findings documented that the overall network structure is made up of a dense cohesive core of physicians and of less connected clinicians who occupy the periphery. A negative association between the physicians' attitudes towards EBM and the coreness they exhibited in the professional network was also found. Network centrality indicators confirmed these results documenting a negative association between physicians' propensity to use EBM and their structural importance in the professional network. Attitudes that physicians show towards EBM are related to the part (core or periphery) of the professional networks to which they belong as well as to their structural importance. By identifying virtuous attitudes and behaviors of professionals within their organizations, policymakers and executives may avoid marginalization and stimulate integration and continuity of care, both within and across the boundaries of healthcare providers.

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns.

PubMed

Lezon, Timothy R; Banavar, Jayanth R; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V

2006-12-12

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sunden, Fanny; Peck, Ariana; Salzman, Julia

Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modesmore » of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes.« less

Modular analysis of the probabilistic genetic interaction network.

PubMed

Hou, Lin; Wang, Lin; Qian, Minping; Li, Dong; Tang, Chao; Zhu, Yunping; Deng, Minghua; Li, Fangting

2011-03-15

Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network. Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules.

Novel subgroups of attention-deficit/hyperactivity disorder identified by topological data analysis and their functional network modular organizations

PubMed Central

Kyeong, Sunghyon; Kim, Jae-Jin; Kim, Eunjoo

2017-01-01

Attention-deficit/hyperactivity disorder (ADHD) is a clinically heterogeneous condition and identification of clinically meaningful subgroups would open up a new window for personalized medicine. Thus, we aimed to identify new clinical phenotypes in children and adolescents with ADHD and to investigate whether neuroimaging findings validate the identified phenotypes. Neuroimaging and clinical data from 67 children with ADHD and 62 typically developing controls (TDCs) from the ADHD-200 database were selected. Clinical measures of ADHD symptoms and intelligence quotient (IQ) were used as input features into a topological data analysis (TDA) to identify ADHD subgroups within our sample. As external validators, graph theoretical measures obtained from the functional connectome were compared to address the biological meaningfulness of the identified subtypes. The TDA identified two unique subgroups of ADHD, labelled as mild symptom ADHD (mADHD) and severe symptom ADHD (sADHD). The output topology shape was repeatedly observed in the independent validation dataset. The graph theoretical analysis showed a decrease in the degree centrality and PageRank in the bilateral posterior cingulate cortex in the sADHD group compared with the TDC group. The mADHD group showed similar patterns of intra- and inter-module connectivity to the sADHD group. Relative to the TDC group, the inter-module connectivity between the default mode network and executive control network were significantly increased in the sADHD group but not in the mADHD group. Taken together, our results show that the data-driven TDA is potentially useful in identifying objective and biologically relevant disease phenotypes in children and adolescents with ADHD. PMID:28829775

Hyperconnectivity in juvenile myoclonic epilepsy: a network analysis.

PubMed

Caeyenberghs, K; Powell, H W R; Thomas, R H; Brindley, L; Church, C; Evans, J; Muthukumaraswamy, S D; Jones, D K; Hamandi, K

2015-01-01

Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients.

Hyperconnectivity in juvenile myoclonic epilepsy: A network analysis

PubMed Central

Caeyenberghs, K.; Powell, H.W.R.; Thomas, R.H.; Brindley, L.; Church, C.; Evans, J.; Muthukumaraswamy, S.D.; Jones, D.K.; Hamandi, K.

2014-01-01

Objective Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Methods Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Results Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Conclusions Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients. PMID:25610771

Differential gene expression analysis in glioblastoma cells and normal human brain cells based on GEO database.

PubMed

Wang, Anping; Zhang, Guibin

2017-11-01

The differentially expressed genes between glioblastoma (GBM) cells and normal human brain cells were investigated to performed pathway analysis and protein interaction network analysis for the differentially expressed genes. GSE12657 and GSE42656 gene chips, which contain gene expression profile of GBM were obtained from Gene Expression Omniub (GEO) database of National Center for Biotechnology Information (NCBI). The 'limma' data packet in 'R' software was used to analyze the differentially expressed genes in the two gene chips, and gene integration was performed using 'RobustRankAggreg' package. Finally, pheatmap software was used for heatmap analysis and Cytoscape, DAVID, STRING and KOBAS were used for protein-protein interaction, Gene Ontology (GO) and KEGG analyses. As results: i) 702 differentially expressed genes were identified in GSE12657, among those genes, 548 were significantly upregulated and 154 were significantly downregulated (p<0.01, fold-change >1), and 1,854 differentially expressed genes were identified in GSE42656, among the genes, 1,068 were significantly upregulated and 786 were significantly downregulated (p<0.01, fold-change >1). A total of 167 differentially expressed genes including 100 upregulated genes and 67 downregulated genes were identified after gene integration, and the genes showed significantly different expression levels in GBM compared with normal human brain cells (p<0.05). ii) Interactions between the protein products of 101 differentially expressed genes were identified using STRING and expression network was established. A key gene, called CALM3, was identified by Cytoscape software. iii) GO enrichment analysis showed that differentially expressed genes were mainly enriched in 'neurotransmitter:sodium symporter activity' and 'neurotransmitter transporter activity', which can affect the activity of neurotransmitter transportation. KEGG pathway analysis showed that the differentially expressed genes were mainly enriched in 'protein processing in endoplasmic reticulum', which can affect protein processing in endoplasmic reticulum. The results showed that: i) 167 differentially expressed genes were identified from two gene chips after integration; and ii) protein interaction network was established, and GO and KEGG pathway analyses were successfully performed to identify and annotate the key gene, which provide new insights for the studies on GBN at gene level.

Social Network Analysis of Biomedical Research Collaboration Networks in a CTSA Institution

PubMed Central

Bian, Jiang; Xie, Mengjun; Topaloglu, Umit; Hudson, Teresa; Eswaran, Hari; Hogan, William

2014-01-01

BACKGROUND The popularity of social networks has triggered a number of research efforts on network analyses of research collaborations in the Clinical and Translational Science Award (CTSA) community. Those studies mainly focus on the general understanding of collaboration networks by measuring common network metrics. More fundamental questions about collaborations still remain unanswered such as recognizing “influential” nodes and identifying potential new collaborations that are most rewarding. METHODS We analyzed biomedical research collaboration networks (RCNs) constructed from a dataset of research grants collected at a CTSA institution (i.e. University of Arkansas for Medical Sciences (UAMS)) in a comprehensive and systematic manner. First, our analysis covers the full spectrum of a RCN study: from network modeling to network characteristics measurement, from key nodes recognition to potential links (collaborations) suggestion. Second, our analysis employs non-conventional model and techniques including a weighted network model for representing collaboration strength, rank aggregation for detecting important nodes, and Random Walk with Restart (RWR) for suggesting new research collaborations. RESULTS By applying our models and techniques to RCNs at UAMS prior to and after the CTSA, we have gained valuable insights that not only reveal the temporal evolution of the network dynamics but also assess the effectiveness of the CTSA and its impact on a research institution. We find that collaboration networks at UAMS are not scale-free but small-world. Quantitative measures have been obtained to evident that the RCNs at UAMS are moving towards favoring multidisciplinary research. Moreover, our link prediction model creates the basis of collaboration recommendations with an impressive accuracy (AUC: 0.990, MAP@3: 1.48 and MAP@5: 1.522). Last but not least, an open-source visual analytical tool for RCNs is being developed and released through Github. CONCLUSIONS Through this study, we have developed a set of techniques and tools for analyzing research collaboration networks and conducted a comprehensive case study focusing on a CTSA institution. Our findings demonstrate the promising future of these techniques and tools in understanding the generative mechanisms of research collaborations and helping identify beneficial collaborations to members in the research community. PMID:24560679

Genomic analyses identify molecular subtypes of pancreatic cancer.

PubMed

Bailey, Peter; Chang, David K; Nones, Katia; Johns, Amber L; Patch, Ann-Marie; Gingras, Marie-Claude; Miller, David K; Christ, Angelika N; Bruxner, Tim J C; Quinn, Michael C; Nourse, Craig; Murtaugh, L Charles; Harliwong, Ivon; Idrisoglu, Senel; Manning, Suzanne; Nourbakhsh, Ehsan; Wani, Shivangi; Fink, Lynn; Holmes, Oliver; Chin, Venessa; Anderson, Matthew J; Kazakoff, Stephen; Leonard, Conrad; Newell, Felicity; Waddell, Nick; Wood, Scott; Xu, Qinying; Wilson, Peter J; Cloonan, Nicole; Kassahn, Karin S; Taylor, Darrin; Quek, Kelly; Robertson, Alan; Pantano, Lorena; Mincarelli, Laura; Sanchez, Luis N; Evers, Lisa; Wu, Jianmin; Pinese, Mark; Cowley, Mark J; Jones, Marc D; Colvin, Emily K; Nagrial, Adnan M; Humphrey, Emily S; Chantrill, Lorraine A; Mawson, Amanda; Humphris, Jeremy; Chou, Angela; Pajic, Marina; Scarlett, Christopher J; Pinho, Andreia V; Giry-Laterriere, Marc; Rooman, Ilse; Samra, Jaswinder S; Kench, James G; Lovell, Jessica A; Merrett, Neil D; Toon, Christopher W; Epari, Krishna; Nguyen, Nam Q; Barbour, Andrew; Zeps, Nikolajs; Moran-Jones, Kim; Jamieson, Nigel B; Graham, Janet S; Duthie, Fraser; Oien, Karin; Hair, Jane; Grützmann, Robert; Maitra, Anirban; Iacobuzio-Donahue, Christine A; Wolfgang, Christopher L; Morgan, Richard A; Lawlor, Rita T; Corbo, Vincenzo; Bassi, Claudio; Rusev, Borislav; Capelli, Paola; Salvia, Roberto; Tortora, Giampaolo; Mukhopadhyay, Debabrata; Petersen, Gloria M; Munzy, Donna M; Fisher, William E; Karim, Saadia A; Eshleman, James R; Hruban, Ralph H; Pilarsky, Christian; Morton, Jennifer P; Sansom, Owen J; Scarpa, Aldo; Musgrove, Elizabeth A; Bailey, Ulla-Maja Hagbo; Hofmann, Oliver; Sutherland, Robert L; Wheeler, David A; Gill, Anthony J; Gibbs, Richard A; Pearson, John V; Waddell, Nicola; Biankin, Andrew V; Grimmond, Sean M

2016-03-03

Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

Theoretical reflections on governance in health regions.

PubMed

Bretas, Nilo; Shimizu, Helena Eri

2017-04-01

This article analyzes governance in health regions, through the contributions of two studies: one on a governance model and the other on duties in the management of public policies networks. The former conducted a meta-analysis of 137 case studies in the literature on collaborative governance aimed at preparing an explanatory and analytical model. Authors identified critical variables that will influence the results: a previous history of conflict or cooperation, incentives for participation, power imbalances, leadership and institutional design. They also identified key factors: face-to-face dialogue, trust building and development of commitment and shared vision. The latter study examined networks of public policies in the analytic tradition and the perspective of governance, incorporating concepts from the field of political science, economics and interorganizational relations, in order to support the management of public policies networks. The study identified network management as equivalent to a strategic game involving functions: network activation, framework of relations, intermediation, facilitation and consensus building and mediation and arbitration. The combination of the two reflections provides a conceptual reference for better understanding of governance in health regions.

Applying network theory to animal movements to identify properties of landscape space use.

PubMed

Bastille-Rousseau, Guillaume; Douglas-Hamilton, Iain; Blake, Stephen; Northrup, Joseph M; Wittemyer, George

2018-04-01

Network (graph) theory is a popular analytical framework to characterize the structure and dynamics among discrete objects and is particularly effective at identifying critical hubs and patterns of connectivity. The identification of such attributes is a fundamental objective of animal movement research, yet network theory has rarely been applied directly to animal relocation data. We develop an approach that allows the analysis of movement data using network theory by defining occupied pixels as nodes and connection among these pixels as edges. We first quantify node-level (local) metrics and graph-level (system) metrics on simulated movement trajectories to assess the ability of these metrics to pull out known properties in movement paths. We then apply our framework to empirical data from African elephants (Loxodonta africana), giant Galapagos tortoises (Chelonoidis spp.), and mule deer (Odocoileous hemionus). Our results indicate that certain node-level metrics, namely degree, weight, and betweenness, perform well in capturing local patterns of space use, such as the definition of core areas and paths used for inter-patch movement. These metrics were generally applicable across data sets, indicating their robustness to assumptions structuring analysis or strategies of movement. Other metrics capture local patterns effectively, but were sensitive to specified graph properties, indicating case specific applications. Our analysis indicates that graph-level metrics are unlikely to outperform other approaches for the categorization of general movement strategies (central place foraging, migration, nomadism). By identifying critical nodes, our approach provides a robust quantitative framework to identify local properties of space use that can be used to evaluate the effect of the loss of specific nodes on range wide connectivity. Our network approach is intuitive, and can be implemented across imperfectly sampled or large-scale data sets efficiently, providing a framework for conservationists to analyze movement data. Functions created for the analyses are available within the R package moveNT. © 2018 by the Ecological Society of America.

Whole-exome sequencing in obsessive-compulsive disorder identifies rare mutations in immunological and neurodevelopmental pathways

PubMed Central

Cappi, C; Brentani, H; Lima, L; Sanders, S J; Zai, G; Diniz, B J; Reis, V N S; Hounie, A G; Conceição do Rosário, M; Mariani, D; Requena, G L; Puga, R; Souza-Duran, F L; Shavitt, R G; Pauls, D L; Miguel, E C; Fernandez, T V

2016-01-01

Studies of rare genetic variation have identified molecular pathways conferring risk for developmental neuropsychiatric disorders. To date, no published whole-exome sequencing studies have been reported in obsessive-compulsive disorder (OCD). We sequenced all the genome coding regions in 20 sporadic OCD cases and their unaffected parents to identify rare de novo (DN) single-nucleotide variants (SNVs). The primary aim of this pilot study was to determine whether DN variation contributes to OCD risk. To this aim, we evaluated whether there is an elevated rate of DN mutations in OCD, which would justify this approach toward gene discovery in larger studies of the disorder. Furthermore, to explore functional molecular correlations among genes with nonsynonymous DN SNVs in OCD probands, a protein–protein interaction (PPI) network was generated based on databases of direct molecular interactions. We applied Degree-Aware Disease Gene Prioritization (DADA) to rank the PPI network genes based on their relatedness to a set of OCD candidate genes from two OCD genome-wide association studies (Stewart et al., 2013; Mattheisen et al., 2014). In addition, we performed a pathway analysis with genes from the PPI network. The rate of DN SNVs in OCD was 2.51 × 10−8 per base per generation, significantly higher than a previous estimated rate in unaffected subjects using the same sequencing platform and analytic pipeline. Several genes harboring DN SNVs in OCD were highly interconnected in the PPI network and ranked high in the DADA analysis. Nearly all the DN SNVs in this study are in genes expressed in the human brain, and a pathway analysis revealed enrichment in immunological and central nervous system functioning and development. The results of this pilot study indicate that further investigation of DN variation in larger OCD cohorts is warranted to identify specific risk genes and to confirm our preliminary finding with regard to PPI network enrichment for particular biological pathways and functions. PMID:27023170

Novel integrative genomic tool for interrogating lithium response in bipolar disorder

PubMed Central

Hunsberger, J G; Chibane, F L; Elkahloun, A G; Henderson, R; Singh, R; Lawson, J; Cruceanu, C; Nagarajan, V; Turecki, G; Squassina, A; Medeiros, C D; Del Zompo, M; Rouleau, G A; Alda, M; Chuang, D-M

2015-01-01

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery. PMID:25646593

Novel integrative genomic tool for interrogating lithium response in bipolar disorder.

PubMed

Hunsberger, J G; Chibane, F L; Elkahloun, A G; Henderson, R; Singh, R; Lawson, J; Cruceanu, C; Nagarajan, V; Turecki, G; Squassina, A; Medeiros, C D; Del Zompo, M; Rouleau, G A; Alda, M; Chuang, D-M

2015-02-03

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

PubMed Central

Chasman, Deborah; Walters, Kevin B.; Lopes, Tiago J. S.; Eisfeld, Amie J.; Kawaoka, Yoshihiro; Roy, Sushmita

2016-01-01

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. PMID:27403523

Morphine Regulated Synaptic Networks Revealed by Integrated Proteomics and Network Analysis*

PubMed Central

Stockton, Steven D.; Gomes, Ivone; Liu, Tong; Moraje, Chandrakala; Hipólito, Lucia; Jones, Matthew R.; Ma'ayan, Avi; Morón, Jose A.; Li, Hong; Devi, Lakshmi A.

2015-01-01

Despite its efficacy, the use of morphine for the treatment of chronic pain remains limited because of the rapid development of tolerance, dependence and ultimately addiction. These undesired effects are thought to be because of alterations in synaptic transmission and neuroplasticity within the reward circuitry including the striatum. In this study we used subcellular fractionation and quantitative proteomics combined with computational approaches to investigate the morphine-induced protein profile changes at the striatal postsynaptic density. Over 2,600 proteins were identified by mass spectrometry analysis of subcellular fractions enriched in postsynaptic density associated proteins from saline or morphine-treated striata. Among these, the levels of 34 proteins were differentially altered in response to morphine. These include proteins involved in G-protein coupled receptor signaling, regulation of transcription and translation, chaperones, and protein degradation pathways. The altered expression levels of several of these proteins was validated by Western blotting analysis. Using Genes2Fans software suite we connected the differentially expressed proteins with proteins identified within the known background protein-protein interaction network. This led to the generation of a network consisting of 116 proteins with 40 significant intermediates. To validate this, we confirmed the presence of three proteins predicted to be significant intermediates: caspase-3, receptor-interacting serine/threonine protein kinase 3 and NEDD4 (an E3-ubiquitin ligase identified as a neural precursor cell expressed developmentally down-regulated protein 4). Because this morphine-regulated network predicted alterations in proteasomal degradation, we examined the global ubiquitination state of postsynaptic density proteins and found it to be substantially altered. Together, these findings suggest a role for protein degradation and for the ubiquitin/proteasomal system in the etiology of opiate dependence and addiction. PMID:26149443

Network-based co-expression analysis for exploring the potential diagnostic biomarkers of metastatic melanoma.

PubMed

Wang, Li-Xin; Li, Yang; Chen, Guan-Zhi

2018-01-01

Metastatic melanoma is an aggressive skin cancer and is one of the global malignancies with high mortality and morbidity. It is essential to identify and verify diagnostic biomarkers of early metastatic melanoma. Previous studies have systematically assessed protein biomarkers and mRNA-based expression characteristics. However, molecular markers for the early diagnosis of metastatic melanoma have not been identified. To explore potential regulatory targets, we have analyzed the gene microarray expression profiles of malignant melanoma samples by co-expression analysis based on the network approach. The differentially expressed genes (DEGs) were screened by the EdgeR package of R software. A weighted gene co-expression network analysis (WGCNA) was used for the identification of DEGs in the special gene modules and hub genes. Subsequently, a protein-protein interaction network was constructed to extract hub genes associated with gene modules. Finally, twenty-four important hub genes (RASGRP2, IKZF1, CXCR5, LTB, BLK, LINGO3, CCR6, P2RY10, RHOH, JUP, KRT14, PLA2G3, SPRR1A, KRT78, SFN, CLDN4, IL1RN, PKP3, CBLC, KRT16, TMEM79, KLK8, LYPD3 and LYPD5) were treated as valuable factors involved in the immune response and tumor cell development in tumorigenesis. In addition, a transcriptional regulatory network was constructed for these specific modules or hub genes, and a few core transcriptional regulators were found to be mostly associated with our hub genes, including GATA1, STAT1, SP1, and PSG1. In summary, our findings enhance our understanding of the biological process of malignant melanoma metastasis, enabling us to identify specific genes to use for diagnostic and prognostic markers and possibly for targeted therapy.

What Makes a Tweet Fly? Analysis of Twitter Messaging at Four Infection Control Conferences.

PubMed

Mitchell, Brett G; Russo, Philip L; Otter, Jonathan A; Kiernan, Martin A; Aveling, Landon

2017-11-01

OBJECTIVE To examine tweeting activity, networks, and common topics mentioned on Twitter at 4 international infection control and infectious disease conferences. DESIGN A cross-sectional study. METHODS An independent company was commissioned to undertake a Twitter 'trawl' each month between July 1, 2016, and November 31, 2016. The trawl identified any tweets that contained the official hashtags of the conferences for (1) the UK Infection Prevention Society, (2) IDWeek 2016, (3) the Federation of Infectious Society/Hospital Infection Society, and (4) the Australasian College for Infection Prevention and Control. Topics from each tweet were identified, and an examination of the frequency and timing of tweets was performed. A social network analysis was performed to illustrate connections between users. A multivariate binary logistic regression model was developed to explore the predictors of 'retweets.' RESULTS In total, 23,718 tweets were identified as using 1 of the 2 hashtags of interest. The results demonstrated that the most tweets were posted during the conferences. Network analysis demonstrated a diversity of twitter networks. A link to a web address was a significant predictor of whether a tweet would be retweeted (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.9-2.1). Other significant factors predicting a retweet included tweeting on topics such as Clostridium difficile (OR, 2.0; 95% CI, 1.7-2.4) and the media (OR, 1.8; 95% CI, 1.6-2.0). Tweets that contained a picture were significantly less likely to be retweeted (OR, 0.06; 95% CI, 0.05-0.08). CONCLUSION Twitter is a useful tool for information sharing and networking at infection control conferences. Infect Control Hosp Epidemiol 2017;38:1271-1276.

Prioritization of Epilepsy Associated Candidate Genes by Convergent Analysis

PubMed Central

Jia, Peilin; Ewers, Jeffrey M.; Zhao, Zhongming

2011-01-01

Background Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs) that are more likely to be associated with epilepsy. The responsible gene(s) within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research. Methodology/Principal Findings In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways. Conclusions/Significance The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The strategy can be applied for the study of other complex diseases. PMID:21390307

Prioritization of epilepsy associated candidate genes by convergent analysis.

PubMed

Jia, Peilin; Ewers, Jeffrey M; Zhao, Zhongming

2011-02-24

Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs) that are more likely to be associated with epilepsy. The responsible gene(s) within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research. In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways. The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The strategy can be applied for the study of other complex diseases.

SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data.

PubMed

Li, Jun; Roebuck, Paul; Grünewald, Stefan; Liang, Han

2012-07-01

An important task in biomedical research is identifying biomarkers that correlate with patient clinical data, and these biomarkers then provide a critical foundation for the diagnosis and treatment of disease. Conventionally, such an analysis is based on individual genes, but the results are often noisy and difficult to interpret. Using a biological network as the searching platform, network-based biomarkers are expected to be more robust and provide deep insights into the molecular mechanisms of disease. We have developed a novel bioinformatics web server for identifying network-based biomarkers that most correlate with patient survival data, SurvNet. The web server takes three input files: one biological network file, representing a gene regulatory or protein interaction network; one molecular profiling file, containing any type of gene- or protein-centred high-throughput biological data (e.g. microarray expression data or DNA methylation data); and one patient survival data file (e.g. patients' progression-free survival data). Given user-defined parameters, SurvNet will automatically search for subnetworks that most correlate with the observed patient survival data. As the output, SurvNet will generate a list of network biomarkers and display them through a user-friendly interface. SurvNet can be accessed at http://bioinformatics.mdanderson.org/main/SurvNet.

Moving from theory to practice: A participatory social network mapping approach to address unmet need for family planning in Benin.

PubMed

Igras, Susan; Diakité, Mariam; Lundgren, Rebecka

2017-07-01

In West Africa, social factors influence whether couples with unmet need for family planning act on birth-spacing desires. Tékponon Jikuagou is testing a social network-based intervention to reduce social barriers by diffusing new ideas. Individuals and groups judged socially influential by their communities provide entrée to networks. A participatory social network mapping methodology was designed to identify these diffusion actors. Analysis of monitoring data, in-depth interviews, and evaluation reports assessed the methodology's acceptability to communities and staff and whether it produced valid, reliable data to identify influential individuals and groups who diffuse new ideas through their networks. Results indicated the methodology's acceptability. Communities were actively and equitably engaged. Staff appreciated its ability to yield timely, actionable information. The mapping methodology also provided valid and reliable information by enabling communities to identify highly connected and influential network actors. Consistent with social network theory, this methodology resulted in the selection of informal groups and individuals in both informal and formal positions. In-depth interview data suggest these actors were diffusing new ideas, further confirming their influence/connectivity. The participatory methodology generated insider knowledge of who has social influence, challenging commonly held assumptions. Collecting and displaying information fostered staff and community learning, laying groundwork for social change.

Network Indicators of the Social Ecology of Adolescents in Relative and Non-Relative Foster Households

PubMed Central

Kothari, Brianne H.; McBeath, Bowen; Sorenson, Paul; Bank, Lew

2016-01-01

Though the presence, composition, and quality of social relationships—particularly as found in family networks—has an important influence on adolescent well-being, little is known about the social ecology of youth in foster care. This study examined the social networks of foster youth participating in a large RCT of an intervention for siblings in foster care. Youth reported on the people they lived with and the relatives they were in contact with, which provided indicators of network size, composition, and relationship quality. Cluster analysis was used to identify five family network profiles for youth living in foster homes. Two identified subgroups reflected robust family networks where youth were living with relative caregiver(s) and related youth, and also reported multiple family ties outside the household, including with biological parents. The remaining three profiles reflected youth reports of fewer family connections within or beyond the foster household, with distinctions by whether they lived with siblings and/or reported having positive relationships with their mothers and/or fathers. The identified network profiles were validated using youth- and caregiver-reported measures of mental health functioning, with increased caregiver report of post-traumatic stress symptoms indicated for the three subgroups that were not characterized by a robust family network. PMID:28736465

Exploring the Epileptic Brain Network Using Time-Variant Effective Connectivity and Graph Theory.

PubMed

Storti, Silvia Francesca; Galazzo, Ilaria Boscolo; Khan, Sehresh; Manganotti, Paolo; Menegaz, Gloria

2017-09-01

The application of time-varying measures of causality between source time series can be very informative to elucidate the direction of communication among the regions of an epileptic brain. The aim of the study was to identify the dynamic patterns of epileptic networks in focal epilepsy by applying multivariate adaptive directed transfer function (ADTF) analysis and graph theory to high-density electroencephalographic recordings. The cortical network was modeled after source reconstruction and topology modulations were detected during interictal spikes. First a distributed linear inverse solution, constrained to the individual grey matter, was applied to the averaged spikes and the mean source activity over 112 regions, as identified by the Harvard-Oxford Atlas, was calculated. Then, the ADTF, a dynamic measure of causality, was used to quantify the connectivity strength between pairs of regions acting as nodes in the graph, and the measure of node centrality was derived. The proposed analysis was effective in detecting the focal regions as well as in characterizing the dynamics of the spike propagation, providing evidence of the fact that the node centrality is a reliable feature for the identification of the epileptogenic zones. Validation was performed by multimodal analysis as well as from surgical outcomes. In conclusion, the time-variant connectivity analysis applied to the epileptic patients can distinguish the generator of the abnormal activity from the propagation spread and identify the connectivity pattern over time.

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

PubMed

López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

2015-04-15

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

Nontargeted diagnostic ion network analysis (NINA): A software to streamline the analytical workflow for untargeted characterization of natural medicines.

PubMed

Ye, Hui; Zhu, Lin; Sun, Di; Luo, Xiaozhuo; Lu, Gaoyuan; Wang, Hong; Wang, Jing; Cao, Guoxiu; Xiao, Wei; Wang, Zhenzhong; Wang, Guangji; Hao, Haiping

2016-11-30

The characterization of herbal prescriptions serves as a foundation for quality control and regulation of herbal medicines. Previously, the characterization of herbal chemicals from natural medicines often relied on the analysis of signature fragment ions from the acquired tandem mass spectrometry (MS/MS) spectra with prior knowledge of the herbal species present in the herbal prescriptions of interest. Nevertheless, such an approach is often limited to target components, and it risks missing the critical components that we have no prior knowledge of. We previously reported a "diagnostic ion-guided network bridging" strategy. It is a generally applicable and robust approach to analyze unknown substances from complex mixtures in an untargeted manner. In this study, we have developed a standalone software named "Nontargeted Diagnostic Ion Network Analysis (NINA)" with a graphical user interface based on a strategy for post-acquisition data analysis. NINA allows one to rapidly determine the nontargeted diagnostic ions (NIs) by summarizing all of the fragment ions shared by the precursors from the acquired MS/MS spectra. A NI-guided network using bridging components that possess two or more NIs can then be established via NINA. With such a network, we could sequentially identify the structures of all the NIs once a single compound has been identified de novo. The structures of NIs can then be used as "priori" knowledge to narrow the candidates containing the sub-structure of the corresponding NI from the database hits. Subsequently, we applied the NINA software to the characterization of a model herbal prescription, Re-Du-Ning injection, and rapidly identified 56 herbal chemicals from the prescription using an ultra-performance liquid chromatography quadrupole time-of-flight system in the negative mode with no knowledge of the herbal species or herbal chemicals in the mixture. Therefore, we believe the applications of NINA will greatly facilitate the characterization of complex mixtures, such as natural medicines, especially when no advance information is available. In addition to herbal medicines, the NINA-based workflow will also benefit many other fields, such as environmental analysis, nutritional science, and forensic analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

An iterative network partition algorithm for accurate identification of dense network modules

PubMed Central

Sun, Siqi; Dong, Xinran; Fu, Yao; Tian, Weidong

2012-01-01

A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity has the advantage of preserving the primary network structure of the undetected modules. Thus, we have developed a simple iterative Network Partition (iNP) algorithm to partition a network. The iNP algorithm provides a general framework in which any modularity-based algorithm can be implemented in the network partition step. Here, we tested iNP with three modularity-based algorithms: multi-step greedy (MSG), spectral clustering and Qcut. Compared with the original three methods, iNP achieved a significant improvement in the quality of network partition in a benchmark study with simulated networks, identified more modules with significantly better enrichment of functionally related genes in both yeast protein complex network and breast cancer gene co-expression network, and discovered more cancer-specific modules in the cancer gene co-expression network. As such, iNP should have a broad application as a general method to assist in the analysis of biological networks. PMID:22121225

Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

PubMed

Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

2017-08-01

This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

An Investigation of the Differences and Similarities between Generated Small-World Networks for Right- and Left-Hand Motor Imageries.

PubMed

Zhang, Jiang; Li, Yuyao; Chen, Huafu; Ding, Jurong; Yuan, Zhen

2016-11-04

In this study, small-world network analysis was performed to identify the similarities and differences between functional brain networks for right- and left-hand motor imageries (MIs). First, Pearson correlation coefficients among the nodes within the functional brain networks from healthy subjects were calculated. Then, small-world network indicators, including the clustering coefficient, the average path length, the global efficiency, the local efficiency, the average node degree, and the small-world index, were generated for the functional brain networks during both right- and left-hand MIs. We identified large differences in the small-world network indicators between the functional networks during MI and in the random networks. More importantly, the functional brain networks underlying the right- and left-hand MIs exhibited similar small-world properties in terms of the clustering coefficient, the average path length, the global efficiency, and the local efficiency. By contrast, the right- and left-hand MI brain networks showed differences in small-world characteristics, including indicators such as the average node degree and the small-world index. Interestingly, our findings also suggested that the differences in the activity intensity and range, the average node degree, and the small-world index of brain networks between the right- and left-hand MIs were associated with the asymmetry of brain functions.

Identification of GRB2 and GAB1 Coexpression as an Unfavorable Prognostic Factor for Hepatocellular Carcinoma by a Combination of Expression Profile and Network Analysis

PubMed Central

Yang, Mei; Wang, Danhua; Yu, Lingxiang; Guo, Chaonan; Guo, Xiaodong; Lin, Na

2013-01-01

Aim To screen novel markers for hepatocellular carcinoma (HCC) by a combination of expression profile, interaction network analysis and clinical validation. Methods HCC significant molecules which are differentially expressed or had genetic variations in HCC tissues were obtained from five existing HCC related databases (OncoDB.HCC, HCC.net, dbHCCvar, EHCO and Liverome). Then, the protein-protein interaction (PPI) network of these molecules was constructed. Three topological features of the network ('Degree', 'Betweenness', and 'Closeness') and the k-core algorithm were used to screen candidate HCC markers which play crucial roles in tumorigenesis of HCC. Furthermore, the clinical significance of two candidate HCC markers growth factor receptor-bound 2 (GRB2) and GRB2-associated-binding protein 1 (GAB1) was validated. Results In total, 6179 HCC significant genes and 977 HCC significant proteins were collected from existing HCC related databases. After network analysis, 331 candidate HCC markers were identified. Especially, GAB1 has the highest k-coreness suggesting its central localization in HCC related network, and the interaction between GRB2 and GAB1 has the largest edge-betweenness implying it may be biologically important to the function of HCC related network. As the results of clinical validation, the expression levels of both GRB2 and GAB1 proteins were significantly higher in HCC tissues than those in their adjacent nonneoplastic tissues. More importantly, the combined GRB2 and GAB1 protein expression was significantly associated with aggressive tumor progression and poor prognosis in patients with HCC. Conclusion This study provided an integrative analysis by combining expression profile and interaction network analysis to identify a list of biologically significant HCC related markers and pathways. Further experimental validation indicated that the aberrant expression of GRB2 and GAB1 proteins may be strongly related to tumor progression and prognosis in patients with HCC. The overexpression of GRB2 in combination with upregulation of GAB1 may be an unfavorable prognostic factor for HCC. PMID:24391994

Mapping U.S. cattle shipment networks: Spatial and temporal patterns of trade communities from 2009 to 2011.

PubMed

Gorsich, Erin E; Luis, Angela D; Buhnerkempe, Michael G; Grear, Daniel A; Portacci, Katie; Miller, Ryan S; Webb, Colleen T

2016-11-01

The application of network analysis to cattle shipments broadens our understanding of shipment patterns beyond pairwise interactions to the network as a whole. Such a quantitative description of cattle shipments in the U.S. can identify trade communities, describe temporal shipment patterns, and inform the design of disease surveillance and control strategies. Here, we analyze a longitudinal dataset of beef and dairy cattle shipments from 2009 to 2011 in the United States to characterize communities within the broader cattle shipment network, which are groups of counties that ship mostly to each other. Because shipments occur over time, we aggregate the data at various temporal scales to examine the consistency of network and community structure over time. Our results identified nine large (>50 counties) communities based on shipments of beef cattle in 2009 aggregated into an annual network and nine large communities based on shipments of dairy cattle. The size and connectance of the shipment network was highly dynamic; monthly networks were smaller than yearly networks and revealed seasonal shipment patterns consistent across years. Comparison of the shipment network over time showed largely consistent shipping patterns, such that communities identified on annual networks of beef and diary shipments from 2009 still represented 41-95% of shipments in monthly networks from 2009 and 41-66% of shipments from networks in 2010 and 2011. The temporal aspects of cattle shipments suggest that future applications of the U.S. cattle shipment network should consider seasonal shipment patterns. However, the consistent within-community shipping patterns indicate that yearly communities could provide a reasonable way to group regions for management. Copyright © 2016 Elsevier B.V. All rights reserved.

The Key Roles in the Informal Organization: A Network Analysis Perspective

ERIC Educational Resources Information Center

de Toni, Alberto F.; Nonino, Fabio

2010-01-01

Purpose: The purpose of this paper is to identify the key roles embedded in the informal organizational structure (informal networks) and to outline their contribution in the companies' performance. A major objective of the research is to find and characterize a new key informal role that synthesises problem solving, expertise, and accessibility…

Genome-wide association study and gene network analysis of fertility, retained placenta, and metritis in US Holstein cattle

USDA-ARS?s Scientific Manuscript database

The objectives of this research were to identify genes, genomic regions, and gene networks associated with three measures of fertility (daughter pregnancy rate, DPR; heifer conception rate, HCR; and cow conception rate, CCR) and two measures of reproductive health (metritis, METR; and retained place...

Large-scale network dysfunction in Major Depressive Disorder: Meta-analysis of resting-state functional connectivity

PubMed Central

Kaiser, Roselinde H.; Andrews-Hanna, Jessica R.; Wager, Tor D.; Pizzagalli, Diego A.

2015-01-01

IMPORTANCE Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. OBJECTIVE To investigate network dysfunction in MDD through the first meta-analysis of rsFC studies. DATA SOURCES Seed-based voxel-wise rsFC studies comparing MDD with healthy individuals (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web-of-Science, EMBASE), and authors contacted for additional data. STUDY SELECTION Twenty-seven datasets from 25 publications (556 MDD adults/teens; 518 controls) were included in the meta-analysis. DATA EXTRACTION AND SYNTHESIS Coordinates of seed regions-of-interest and between-group effects were extracted. Seeds were categorized into “seed-networks” by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive, or reduced negative, connectivity) or hypoconnectivity (increased negative, or reduced positive, connectivity) with each seed-network. RESULTS MDD was characterized by hypoconnectivity within the frontoparietal network (FN), a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network (DAN) involved in attending to the external environment. MDD was also associated with hyperconnectivity within the default network (DN), a network believed to support internally-oriented and self-referential thought, and hyperconnectivity between FN control systems and regions of DN. Finally, MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions. CONCLUSIONS AND RELEVANCE Reduced connectivity within frontoparietal control systems, and imbalanced connectivity between control systems and networks involved in internal- or external-attention, may reflect depressive biases towards internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression. PMID:25785575

A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data.

PubMed

Chen Peng; Ao Li

2017-01-01

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

Temporal Lobe and “Default” Hemodynamic Brain Modes Discriminate Between Schizophrenia and Bipolar Disorder

PubMed Central

Calhoun, Vince D.; Maciejewski, Paul K.; Pearlson, Godfrey D.; Kiehl, Kent A.

2009-01-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or “spatial modes” exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder. PMID:17894392

Temporal lobe and "default" hemodynamic brain modes discriminate between schizophrenia and bipolar disorder.

PubMed

Calhoun, Vince D; Maciejewski, Paul K; Pearlson, Godfrey D; Kiehl, Kent A

2008-11-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or "spatial modes" exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

Complex networks in the Euclidean space of communicability distances

NASA Astrophysics Data System (ADS)

Estrada, Ernesto

2012-06-01

We study the properties of complex networks embedded in a Euclidean space of communicability distances. The communicability distance between two nodes is defined as the difference between the weighted sum of walks self-returning to the nodes and the weighted sum of walks going from one node to the other. We give some indications that the communicability distance identifies the least crowded routes in networks where simultaneous submission of packages is taking place. We define an index Q based on communicability and shortest path distances, which allows reinterpreting the “small-world” phenomenon as the region of minimum Q in the Watts-Strogatz model. It also allows the classification and analysis of networks with different efficiency of spatial uses. Consequently, the communicability distance displays unique features for the analysis of complex networks in different scenarios.

Analysis of complex neural circuits with nonlinear multidimensional hidden state models

PubMed Central

Friedman, Alexander; Slocum, Joshua F.; Tyulmankov, Danil; Gibb, Leif G.; Altshuler, Alex; Ruangwises, Suthee; Shi, Qinru; Toro Arana, Sebastian E.; Beck, Dirk W.; Sholes, Jacquelyn E. C.; Graybiel, Ann M.

2016-01-01

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain. PMID:27222584

Shared molecular networks in orofacial and neural tube development.

PubMed

Kousa, Youssef A; Mansour, Tamer A; Seada, Haitham; Matoo, Samaneh; Schutte, Brian C

2017-01-30

Single genetic variants can affect multiple tissues during development. Thus it is possible that disruption of shared gene regulatory networks might underlie syndromic presentations. In this study, we explore this idea through examination of two critical developmental programs that control orofacial and neural tube development and identify shared regulatory factors and networks. Identification of these networks has the potential to yield additional candidate genes for poorly understood developmental disorders and assist in modeling and perhaps managing risk factors to prevent morbidly and mortality. We reviewed the literature to identify genes common between orofacial and neural tube defects and development. We then conducted a bioinformatic analysis to identify shared molecular targets and pathways in the development of these tissues. Finally, we examine publicly available RNA-Seq data to identify which of these genes are expressed in both tissues during development. We identify common regulatory factors in orofacial and neural tube development. Pathway enrichment analysis shows that folate, cancer and hedgehog signaling pathways are shared in neural tube and orofacial development. Developing neural tissues differentially express mouse exencephaly and cleft palate genes, whereas developing orofacial tissues were enriched for both clefting and neural tube defect genes. These data suggest that key developmental factors and pathways are shared between orofacial and neural tube defects. We conclude that it might be most beneficial to focus on common regulatory factors and pathways to better understand pathology and develop preventative measures for these birth defects. Birth Defects Research 109:169-179, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Neural network post-processing of grayscale optical correlator

NASA Technical Reports Server (NTRS)

Lu, Thomas T; Hughlett, Casey L.; Zhoua, Hanying; Chao, Tien-Hsin; Hanan, Jay C.

2005-01-01

In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis

PubMed Central

Kim, Jongwoon

2017-01-01

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer. PMID:28973975

Parameterization of Keeling's network generation algorithm.

PubMed

Badham, Jennifer; Abbass, Hussein; Stocker, Rob

2008-09-01

Simulation is increasingly being used to examine epidemic behaviour and assess potential management options. The utility of the simulations rely on the ability to replicate those aspects of the social structure that are relevant to epidemic transmission. One approach is to generate networks with desired social properties. Recent research by Keeling and his colleagues has generated simulated networks with a range of properties, and examined the impact of these properties on epidemic processes occurring over the network. However, published work has included only limited analysis of the algorithm itself and the way in which the network properties are related to the algorithm parameters. This paper identifies some relationships between the algorithm parameters and selected network properties (mean degree, degree variation, clustering coefficient and assortativity). Our approach enables users of the algorithm to efficiently generate a network with given properties, thereby allowing realistic social networks to be used as the basis of epidemic simulations. Alternatively, the algorithm could be used to generate social networks with a range of property values, enabling analysis of the impact of these properties on epidemic behaviour.

Cross-cohort analysis identifies a TEAD4 ↔ MYCN positive-feedback loop as the core regulatory element of high-risk neuroblastoma. | Office of Cancer Genomics

Cancer.gov

High-risk neuroblastomas show a paucity of recurrent somatic mutations at diagnosis. As a result, the molecular basis for this aggressive phenotype remains elusive. Recent progress in regulatory network analysis helped us elucidate disease-driving mechanisms downstream of genomic alterations, including recurrent chromosomal alterations. Our analysis identified three molecular subtypes of high-risk neuroblastomas, consistent with chromosomal alterations, and identified subtype-specific master regulator (MR) proteins that were conserved across independent cohorts.

Detecting anomalous traders using multi-slice network analysis

NASA Astrophysics Data System (ADS)

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2017-05-01

Manipulation is an important issue for both developed and emerging stock markets. Many efforts have been made to detect manipulation in stock market. However, it is still an open problem to identify the fraudulent traders, especially when they collude with each other. In this paper, we focus on the problem of identifying anomalous traders using the transaction data of 8 manipulated stocks and 42 non-manipulated stocks during a one-year period. For each stock, we construct a multi-slice trading network to characterize the daily trading behavior and the cross-day participation of each trader. Comparing the multi-slice trading network of manipulated stocks and non-manipulated stocks with their randomized version, we find that manipulated stocks exhibit high number of trader pairs that trade with each other in multiple days and high deviation from randomized network at correlation between trading frequency and trading activity. These findings are effective at distinguishing manipulated stocks from non-manipulated ones and at identifying anomalous traders.

Degree-strength correlation reveals anomalous trading behavior.

PubMed

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Wang, Zhao-Yang

2012-01-01

Manipulation is an important issue for both developed and emerging stock markets. Many efforts have been made to detect manipulation in stock markets. However, it is still an open problem to identify the fraudulent traders, especially when they collude with each other. In this paper, we focus on the problem of identifying the anomalous traders using the transaction data of eight manipulated stocks and forty-four non-manipulated stocks during a one-year period. By analyzing the trading networks of stocks, we find that the trading networks of manipulated stocks exhibit significantly higher degree-strength correlation than the trading networks of non-manipulated stocks and the randomized trading networks. We further propose a method to detect anomalous traders of manipulated stocks based on statistical significance analysis of degree-strength correlation. Experimental results demonstrate that our method is effective at distinguishing the manipulated stocks from non-manipulated ones. Our method outperforms the traditional weight-threshold method at identifying the anomalous traders in manipulated stocks. More importantly, our method is difficult to be fooled by colluded traders.

Using Co-authorship Networks to Map and Analyse Global Neglected Tropical Disease Research with an Affiliation to Germany

PubMed Central

Bender, Max Ernst; Edwards, Suzanne; von Philipsborn, Peter; Steinbeis, Fridolin; Keil, Thomas; Tinnemann, Peter

2015-01-01

Background Research on Neglected Tropical Diseases (NTDs) has increased in recent decades, and significant need-gaps in diagnostic and treatment tools remain. Analysing bibliometric data from published research is a powerful method for revealing research efforts, partnerships and expertise. We aim to identify and map NTD research networks in Germany and their partners abroad to enable an informed and transparent evaluation of German contributions to NTD research. Methodology/Principal Findings A SCOPUS database search for articles with German author affiliations that were published between 2002 and 2012 was conducted for kinetoplastid and helminth diseases. Open-access tools were used for data cleaning and scientometrics (OpenRefine), geocoding (OpenStreetMaps) and to create (Table2Net), visualise and analyse co-authorship networks (Gephi). From 26,833 publications from around the world that addressed 11 diseases, we identified 1,187 (4.4%) with at least one German author affiliation, and we processed 972 publications for the five most published-about diseases. Of those, we extracted 4,007 individual authors and 863 research institutions to construct co-author networks. The majority of co-authors outside Germany were from high-income countries and Brazil. Collaborations with partners on the African continent remain scattered. NTD research within Germany was distributed among 220 research institutions. We identified strong performers on an individual level by using classic parameters (number of publications, h-index) and social network analysis parameters (betweenness centrality). The research network characteristics varied strongly between diseases. Conclusions/Significance The share of NTD publications with German affiliations is approximately half of its share in other fields of medical research. This finding underlines the need to identify barriers and expand Germany’s otherwise strong research activities towards NTDs. A geospatial analysis of research collaborations with partners abroad can support decisions to strengthen research capacity, particularly in low- and middle-income countries, which were less involved in collaborations than high-income countries. Identifying knowledge hubs within individual researcher networks complements traditional scientometric indicators that are used to identify opportunities for collaboration. Using free tools to analyse research processes and output could facilitate data-driven health policies. Our findings contribute to the prioritisation of efforts in German NTD research at a time of impending local and global policy decisions. PMID:26719978

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Express path analysis identifies a tyrosine kinase Src-centric network regulating divergent host responses to Mycobacterium tuberculosis infection.

PubMed

Karim, Ahmad Faisal; Chandra, Pallavi; Chopra, Aanchal; Siddiqui, Zaved; Bhaskar, Ashima; Singh, Amit; Kumar, Dhiraj

2011-11-18

Global gene expression profiling has emerged as a major tool in understanding complex response patterns of biological systems to perturbations. However, a lack of unbiased analytical approaches has restricted the utility of complex microarray data to gain novel system level insights. Here we report a strategy, express path analysis (EPA), that helps to establish various pathways differentially recruited to achieve specific cellular responses under contrasting environmental conditions in an unbiased manner. The analysis superimposes differentially regulated genes between contrasting environments onto the network of functional protein associations followed by a series of iterative enrichments and network analysis. To test the utility of the approach, we infected THP1 macrophage cells with a virulent Mycobacterium tuberculosis strain (H37Rv) or the attenuated non-virulent strain H37Ra as contrasting perturbations and generated the temporal global expression profiles. EPA of the results provided details of response-specific and time-dependent host molecular network perturbations. Further analysis identified tyrosine kinase Src as the major regulatory hub discriminating the responses between wild-type and attenuated Mtb infection. We were then able to verify this novel role of Src experimentally and show that Src executes its role through regulating two vital antimicrobial processes of the host cells (i.e. autophagy and acidification of phagolysosome). These results bear significant potential for developing novel anti-tuberculosis therapy. We propose that EPA could prove extremely useful in understanding complex cellular responses for a variety of perturbations, including pathogenic infections.

Semantic integration to identify overlapping functional modules in protein interaction networks

PubMed Central

Cho, Young-Rae; Hwang, Woochang; Ramanathan, Murali; Zhang, Aidong

2007-01-01

Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification. PMID:17650343

The Role of Vitamin D in the Transcriptional Program of Human Pregnancy

PubMed Central

Al-Garawi, Amal; Carey, Vincent J.; Chhabra, Divya; Morrow, Jarrett; Lasky-Su, Jessica; Qiu, Weiliang; Laranjo, Nancy; Litonjua, Augusto A.; Weiss, Scott T.

2016-01-01

Background Patterns of gene expression of human pregnancy are poorly understood. In a trial of vitamin D supplementation in pregnant women, peripheral blood transcriptomes were measured longitudinally on 30 women and used to characterize gene co-expression networks. Objective Studies suggest that increased maternal Vitamin D levels may reduce the risk of asthma in early life, yet the underlying mechanisms have not been examined. In this study, we used a network-based approach to examine changes in gene expression profiles during the course of normal pregnancy and evaluated their association with maternal Vitamin D levels. Design The VDAART study is a randomized clinical trial of vitamin D supplementation in pregnancy for reduction of pediatric asthma risk. The trial enrolled 881 women at 10–18 weeks of gestation. Longitudinal gene expression measures were obtained on thirty pregnant women, using RNA isolated from peripheral blood samples obtained in the first and third trimesters. Differentially expressed genes were identified using significance of analysis of microarrays (SAM), and clustered using a weighted gene co-expression network analysis (WGCNA). Gene-set enrichment was performed to identify major biological pathways. Results Comparison of transcriptional profiles between first and third trimesters of pregnancy identified 5839 significantly differentially expressed genes (FDR<0.05). Weighted gene co-expression network analysis clustered these transcripts into 14 co-expression modules of which two showed significant correlation with maternal vitamin D levels. Pathway analysis of these two modules revealed genes enriched in immune defense pathways and extracellular matrix reorganization as well as genes enriched in notch signaling and transcription factor networks. Conclusion Our data show that gene expression profiles of healthy pregnant women change during the course of pregnancy and suggest that maternal Vitamin D levels influence transcriptional profiles. These alterations of the maternal transcriptome may contribute to fetal immune imprinting and reduce allergic sensitization in early life. Trial Registration clinicaltrials.gov NCT00920621 PMID:27711190

Artificial neural network and classical least-squares methods for neurotransmitter mixture analysis.

PubMed

Schulze, H G; Greek, L S; Gorzalka, B B; Bree, A V; Blades, M W; Turner, R F

1995-02-01

Identification of individual components in biological mixtures can be a difficult problem regardless of the analytical method employed. In this work, Raman spectroscopy was chosen as a prototype analytical method due to its inherent versatility and applicability to aqueous media, making it useful for the study of biological samples. Artificial neural networks (ANNs) and the classical least-squares (CLS) method were used to identify and quantify the Raman spectra of the small-molecule neurotransmitters and mixtures of such molecules. The transfer functions used by a network, as well as the architecture of a network, played an important role in the ability of the network to identify the Raman spectra of individual neurotransmitters and the Raman spectra of neurotransmitter mixtures. Specifically, networks using sigmoid and hyperbolic tangent transfer functions generalized better from the mixtures in the training data set to those in the testing data sets than networks using sine functions. Networks with connections that permit the local processing of inputs generally performed better than other networks on all the testing data sets. and better than the CLS method of curve fitting, on novel spectra of some neurotransmitters. The CLS method was found to perform well on noisy, shifted, and difference spectra.

[Using social network analysis to examine care for older drug users in three major cities in Germany : Results of a pilot study].

PubMed

Kuhn, U; Hofmann, L; Hoff, T; Färber, N

2018-05-04

Compared with the general population, chronic drug addicts already start showing typical aging problems by the age of 40 years. The increasing number of older drug addicts leads to questions of what an adequate health and social care should look like. This discussion particularly takes place in the context of a sufficient integration of different care systems. A sufficient integration requires an improvement in the networking of substance treatment, nursing care and medical care services. The purpose of this study was to investigate the care structure of older people who use drugs and the services involved in a social network analysis. This was a descriptive design of the pilot study. The study objective was to gain first-hand knowledge about the health and social care situation, the quality of care concerning this client group and to identify supply gaps. Therefore, the three regions Cologne, Dusseldorf and Frankfurt/Main were exemplarily examined. The data for the social network analysis was gathered by a quantitative online questionnaire. Therefore, especially central network members were contacted and asked to participate. The survey was conducted in two waves. In total, 65 practitioners of all surveyed cities participated in the second wave. The centrality measures assessed indicated that in all regions institutions of the substance abuse service network hold central positions in terms of conveying information. The moderate density values of the networks suggest that there are sufficient cooperation structures. Care deficits were identified most frequently in the areas of housing and nursing care. The results provide the first systematic insights and a description of the cooperation practice in the care system. Because of the limitations, further research and practice issues are raised.

Alterations in functional brain networks associated with loss-chasing in gambling disorder and cocaine-use disorder.

PubMed

Worhunsky, Patrick D; Potenza, Marc N; Rogers, Robert D

2017-09-01

Continued, persistent gambling to recover accumulating losses, or 'loss-chasing', is a behavioral pattern linked particularly closely to gambling disorder (GD) but may reflect impaired decision-making processes relevant to drug addictions like cocaine-use disorder (CUD). However, little is known regarding the neurocognitive mechanisms of this complex, maladaptive behavior, particularly in individuals with addictive disorders. Seventy participants (25 GD, 18 CUD, and 27 healthy comparison (HC)) completed a loss-chase task during fMRI. Engagement of functional brain networks in response to losing outcomes and during decision-making periods preceding choices to loss-chase or to quit chasing losses were investigated using independent component analysis (ICA). An exploratory factor analysis was performed to examine patterns of coordinated engagement across identified networks. In GD relative to HC and CUD participants, choices to quit chasing were associated with greater engagement of a medial frontal executive-processing network. By comparison, CUD participants exhibited altered engagement of a striato-amygdala motivational network in response to losing outcomes as compared to HC, and during decision-making as compared to GD. Several other networks were differentially engaged during loss-chase relative to quit-chasing choices, but did not differ across participant groups. Exploratory factor analysis identified a system of coordinated activity across prefrontal executive-control networks that was greater in GD and CUD relative to HC participants and was associated with increased chasing persistence across all participants. Results provide evidence of shared and distinct neurobiological mechanisms in substance and behavioral addictions, and lend insight into potential cognitive interventions targeting loss-chasing behavior in GD. Copyright © 2017 Elsevier B.V. All rights reserved.

Topology Analysis of Social Networks Extracted from Literature

PubMed Central

2015-01-01

In a world where complex networks are an increasingly important part of science, it is interesting to question how the new reading of social realities they provide applies to our cultural background and in particular, popular culture. Are authors of successful novels able to reproduce social networks faithful to the ones found in reality? Is there any common trend connecting an author’s oeuvre, or a genre of fiction? Such an analysis could provide new insight on how we, as a culture, perceive human interactions and consume media. The purpose of the work presented in this paper is to define the signature of a novel’s story based on the topological analysis of its social network of characters. For this purpose, an automated tool was built that analyses the dialogs in novels, identifies characters and computes their relationships in a time-dependent manner in order to assess the network’s evolution over the course of the story. PMID:26039072

Methods for evaluating temporal groundwater quality data and results of decadal-scale changes in chloride, dissolved solids, and nitrate concentrations in groundwater in the United States, 1988-2010

USGS Publications Warehouse

Lindsey, Bruce D.; Rupert, Michael G.

2012-01-01

Decadal-scale changes in groundwater quality were evaluated by the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program. Samples of groundwater collected from wells during 1988-2000 - a first sampling event representing the decade ending the 20th century - were compared on a pair-wise basis to samples from the same wells collected during 2001-2010 - a second sampling event representing the decade beginning the 21st century. The data set consists of samples from 1,236 wells in 56 well networks, representing major aquifers and urban and agricultural land-use areas, with analytical results for chloride, dissolved solids, and nitrate. Statistical analysis was done on a network basis rather than by individual wells. Although spanning slightly more or less than a 10-year period, the two-sample comparison between the first and second sampling events is referred to as an analysis of decadal-scale change based on a step-trend analysis. The 22 principal aquifers represented by these 56 networks account for nearly 80 percent of the estimated withdrawals of groundwater used for drinking-water supply in the Nation. Well networks where decadal-scale changes in concentrations were statistically significant were identified using the Wilcoxon-Pratt signed-rank test. For the statistical analysis of chloride, dissolved solids, and nitrate concentrations at the network level, more than half revealed no statistically significant change over the decadal period. However, for networks that had statistically significant changes, increased concentrations outnumbered decreased concentrations by a large margin. Statistically significant increases of chloride concentrations were identified for 43 percent of 56 networks. Dissolved solids concentrations increased significantly in 41 percent of the 54 networks with dissolved solids data, and nitrate concentrations increased significantly in 23 percent of 56 networks. At least one of the three - chloride, dissolved solids, or nitrate - had a statistically significant increase in concentration in 66 percent of the networks. Statistically significant decreases in concentrations were identified in 4 percent of the networks for chloride, 2 percent of the networks for dissolved solids, and 9 percent of the networks for nitrate. A larger percentage of urban land-use networks had statistically significant increases in chloride, dissolved solids, and nitrate concentrations than agricultural land-use networks. In order to assess the magnitude of statistically significant changes, the median of the differences between constituent concentrations from the first full-network sampling event and those from the second full-network sampling event was calculated using the Turnbull method. The largest median decadal increases in chloride concentrations were in networks in the Upper Illinois River Basin (67 mg/L) and in the New England Coastal Basins (34 mg/L), whereas the largest median decadal decrease in chloride concentrations was in the Upper Snake River Basin (1 mg/L). The largest median decadal increases in dissolved solids concentrations were in networks in the Rio Grande Valley (260 mg/L) and the Upper Illinois River Basin (160 mg/L). The largest median decadal decrease in dissolved solids concentrations was in the Apalachicola-Chattahoochee-Flint River Basin (6.0 mg/L). The largest median decadal increases in nitrate as nitrogen (N) concentrations were in networks in the South Platte River Basin (2.0 mg/L as N) and the San Joaquin-Tulare Basins (1.0 mg/L as N). The largest median decadal decrease in nitrate concentrations was in the Santee River Basin and Coastal Drainages (0.63 mg/L). The magnitude of change in networks with statistically significant increases typically was much larger than the magnitude of change in networks with statistically significant decreases. The magnitude of change was greatest for chloride in the urban land-use networks and greatest for dissolved solids and nitrate in the agricultural land-use networks. Analysis of data from all networks combined indicated statistically significant increases for chloride, dissolved solids, and nitrate. Although chloride, dissolved solids, and nitrate concentrations were typically less than the drinking-water standards and guidelines, a statistical test was used to determine whether or not the proportion of samples exceeding the drinking-water standard or guideline changed significantly between the first and second full-network sampling events. The proportion of samples exceeding the U.S. Environmental Protection Agency (USEPA) Secondary Maximum Contaminant Level for dissolved solids (500 milligrams per liter) increased significantly between the first and second full-network sampling events when evaluating all networks combined at the national level. Also, for all networks combined, the proportion of samples exceeding the USEPA Maximum Contaminant Level (MCL) of 10 mg/L as N for nitrate increased significantly. One network in the Delmarva Peninsula had a significant increase in the proportion of samples exceeding the MCL for nitrate. A subset of 261 wells was sampled every other year (biennially) to evaluate decadal-scale changes using a time-series analysis. The analysis of the biennial data set showed that changes were generally similar to the findings from the analysis of decadal-scale change that was based on a step-trend analysis. Because of the small number of wells in a network with biennial data (typically 4-5 wells), the time-series analysis is more useful for understanding water-quality responses to changes in site-specific conditions rather than as an indicator of the change for the entire network.

Applying Multivariate Adaptive Splines to Identify Genes With Expressions Varying After Diagnosis in Microarray Experiments.

PubMed

Duan, Fenghai; Xu, Ye

2017-01-01

To analyze a microarray experiment to identify the genes with expressions varying after the diagnosis of breast cancer. A total of 44 928 probe sets in an Affymetrix microarray data publicly available on Gene Expression Omnibus from 249 patients with breast cancer were analyzed by the nonparametric multivariate adaptive splines. Then, the identified genes with turning points were grouped by K-means clustering, and their network relationship was subsequently analyzed by the Ingenuity Pathway Analysis. In total, 1640 probe sets (genes) were reliably identified to have turning points along with the age at diagnosis in their expression profiling, of which 927 expressed lower after turning points and 713 expressed higher after the turning points. K-means clustered them into 3 groups with turning points centering at 54, 62.5, and 72, respectively. The pathway analysis showed that the identified genes were actively involved in various cancer-related functions or networks. In this article, we applied the nonparametric multivariate adaptive splines method to a publicly available gene expression data and successfully identified genes with expressions varying before and after breast cancer diagnosis.

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

PubMed

Roy, Raktim; Shilpa, P Phani; Bagh, Sangram

2016-09-01

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level. Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

URBAN-NET: A Network-based Infrastructure Monitoring and Analysis System for Emergency Management and Public Safety

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Sangkeun; Chen, Liangzhe; Duan, Sisi

Abstract Critical Infrastructures (CIs) such as energy, water, and transportation are complex networks that are crucial for sustaining day-to-day commodity flows vital to national security, economic stability, and public safety. The nature of these CIs is such that failures caused by an extreme weather event or a man-made incident can trigger widespread cascading failures, sending ripple effects at regional or even national scales. To minimize such effects, it is critical for emergency responders to identify existing or potential vulnerabilities within CIs during such stressor events in a systematic and quantifiable manner and take appropriate mitigating actions. We present here amore » novel critical infrastructure monitoring and analysis system named URBAN-NET. The system includes a software stack and tools for monitoring CIs, pre-processing data, interconnecting multiple CI datasets as a heterogeneous network, identifying vulnerabilities through graph-based topological analysis, and predicting consequences based on what-if simulations along with visualization. As a proof-of-concept, we present several case studies to show the capabilities of our system. We also discuss remaining challenges and future work.« less

Evaluating the intersection of a regional wildlife connectivity network with highways.

PubMed

Cushman, Samuel A; Lewis, Jesse S; Landguth, Erin L

2013-01-01

Reliable predictions of regional-scale population connectivity are needed to prioritize conservation actions. However, there have been few examples of regional connectivity models that are empirically derived and validated. The central goals of this paper were to (1) evaluate the effectiveness of factorial least cost path corridor mapping on an empirical resistance surface in reflecting the frequency of highway crossings by American black bear, (2) predict the location and predicted intensity of use of movement corridors for American black bear, and (3) identify where these corridors cross major highways and rank the intensity of these crossings. We used factorial least cost path modeling coupled with resistant kernel analysis to predict a network of movement corridors across a 30.2 million hectare analysis area in Montana and Idaho, USA. Factorial least cost path corridor mapping was associated with the locations of actual bear highway crossings. We identified corridor-highway intersections and ranked these based on corridor strength. We found that a major wildlife crossing overpass structure was located close to one of the most intense predicted corridors, and that the vast majority of the predicted corridor network was "protected" under federal management. However, narrow, linear corridors connecting the Greater Yellowstone Ecosystem to the rest of the analysis area had limited protection by federal ownership, making these additionally vulnerable to habitat loss and fragmentation. Factorial least cost path modeling coupled with resistant kernel analysis provides detailed, synoptic information about connectivity across populations that vary in distribution and density in complex landscapes. Specifically, our results could be used to quantify the structure of the connectivity network, identify critical linkage nodes and core areas, map potential barriers and fracture zones, and prioritize locations for mitigation, restoration and conservation actions.

Integrative network analysis unveils convergent molecular pathways in Parkinson's disease and diabetes.

PubMed

Santiago, Jose A; Potashkin, Judith A

2013-01-01

Shared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level. Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients. These results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that increased expression of APP in blood may modulate the neurodegenerative phenotype in type 2 diabetes patients.