Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on "support vector machines" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

Better Decomposition Heuristics for the Maximum-Weight Connected Graph Problem Using Betweenness Centrality

NASA Astrophysics Data System (ADS)

Yamamoto, Takanori; Bannai, Hideo; Nagasaki, Masao; Miyano, Satoru

We present new decomposition heuristics for finding the optimal solution for the maximum-weight connected graph problem, which is known to be NP-hard. Previous optimal algorithms for solving the problem decompose the input graph into subgraphs using heuristics based on node degree. We propose new heuristics based on betweenness centrality measures, and show through computational experiments that our new heuristics tend to reduce the number of subgraphs in the decomposition, and therefore could lead to the reduction in computational time for finding the optimal solution. The method is further applied to analysis of biological pathway data.

A new network representation of the metabolism to detect chemical transformation modules.

PubMed

Sorokina, Maria; Medigue, Claudine; Vallenet, David

2015-11-14

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

PubMed Central

Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

2016-01-01

Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation: Contact: maskot@bio.titech.ac.jp PMID:27307627

Topological analysis of metabolic control.

PubMed

Sen, A K

1990-12-01

A topological approach is presented for the analysis of control and regulation in metabolic pathways. In this approach, the control structure of a metabolic pathway is represented by a weighted directed graph. From an inspection of the topology of the graph, the control coefficients of the enzymes are evaluated in a heuristic manner in terms of the enzyme elasticities. The major advantage of the topological approach is that it provides a visual framework for (1) calculating the control coefficients of the enzymes, (2) analyzing the cause-effect relationships of the individual enzymes, (3) assessing the relative importance of the enzymes in metabolic regulation, and (4) simplifying the structure of a given pathway, from a regulatory viewpoint. Results are obtained for (a) an unbranched pathway in the absence of feedback the feedforward regulation and (b) an unbranched pathway with feedback inhibition. Our formulation is based on the metabolic control theory of Kacser and Burns (1973) and Heinrich and Rapoport (1974).

Graph Theoretical Analysis, In Silico Modeling, Synthesis, Anti-Microbial and Anti-TB Evaluation of Novel Quinoxaline Derivatives.

PubMed

Saravanan, Govindaraj; Selvam, Theivendren Panneer; Alagarsamy, Veerachamy; Kunjiappan, Selvaraj; Joshi, Shrinivas D; Indhumathy, Murugan; Kumar, Pandurangan Dinesh

2018-05-01

We designed to synthesize a number of 2-(2-(substituted benzylidene) hydrazinyl)-N-(4-((3-(phenyl imino)-3,4-dihydro quinoxalin-2(1 H)-ylidene)amino) phenyl) acetamide S1-S13: with the hope to obtain more active and less toxic anti-microbial and anti-TB agents. A series of novel quinoxaline Schiff bases S1-S13: were synthesized from o-phenylenediamine and oxalic acid by a multistep synthesis. In present work, we are introducing graph theoretical analysis to identify drug target. In the connection of graph theoretical analysis, we utilised KEGG database and Cytoscape software. All the title compounds were evaluated for their in-vitro anti-microbial activity by using agar well diffusion method at three different concentration levels (50, 100 and 150 µg/ml). The MIC of the compounds was also determined by agar streak dilution method. The identified study report through graph theoretical analysis were highlights that the key virulence factor for pathogenic mycobacteria is a eukaryotic-like serine/threonine protein kinase, termed PknG. All compounds were found to display significant activity against entire tested bacteria and fungi. In addition the synthesized scaffolds were screened for their in vitro antituberculosis (anti-TB) activity against Mycobacterium tuberculosis (Mtb) strain H 37 Ra using standard drug Rifampicin. A number of analogs found markedly potent anti-microbial and anti-TB activity. The relationship between the functional group variation and the biological activity of the evaluated compounds were well discussed. The observed study report was showing that the compound S6: (4-nitro substitution) exhibited most potent effective anti-microbial and anti-TB activity out of various tested compounds. © Georg Thieme Verlag KG Stuttgart · New York.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

PubMed

Laniau, Julie; Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien; Siegel, Anne

2017-01-01

The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests , which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

PubMed Central

Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien

2017-01-01

Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype. PMID:29038751

A Bayesian method for inferring transmission chains in a partially observed epidemic.

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

Marzouk, Youssef M.; Ray, Jaideep

2008-10-01

We present a Bayesian approach for estimating transmission chains and rates in the Abakaliki smallpox epidemic of 1967. The epidemic affected 30 individuals in a community of 74; only the dates of appearance of symptoms were recorded. Our model assumes stochastic transmission of the infections over a social network. Distinct binomial random graphs model intra- and inter-compound social connections, while disease transmission over each link is treated as a Poisson process. Link probabilities and rate parameters are objects of inference. Dates of infection and recovery comprise the remaining unknowns. Distributions for smallpox incubation and recovery periods are obtained from historicalmore » data. Using Markov chain Monte Carlo, we explore the joint posterior distribution of the scalar parameters and provide an expected connectivity pattern for the social graph and infection pathway.« less

Graph-based analysis of kinetics on multidimensional potential-energy surfaces.

PubMed

Okushima, T; Niiyama, T; Ikeda, K S; Shimizu, Y

2009-09-01

The aim of this paper is twofold: one is to give a detailed description of an alternative graph-based analysis method, which we call saddle connectivity graph, for analyzing the global topography and the dynamical properties of many-dimensional potential-energy landscapes and the other is to give examples of applications of this method in the analysis of the kinetics of realistic systems. A Dijkstra-type shortest path algorithm is proposed to extract dynamically dominant transition pathways by kinetically defining transition costs. The applicability of this approach is first confirmed by an illustrative example of a low-dimensional random potential. We then show that a coarse-graining procedure tailored for saddle connectivity graphs can be used to obtain the kinetic properties of 13- and 38-atom Lennard-Jones clusters. The coarse-graining method not only reduces the complexity of the graphs, but also, with iterative use, reveals a self-similar hierarchical structure in these clusters. We also propose that the self-similarity is common to many-atom Lennard-Jones clusters.

Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction.

PubMed

Gawthrop, Peter J

2017-04-01

Engineering systems modeling and analysis based on the bond graph approach has been applied to biomolecular systems. In this context, the notion of a Faraday-equivalent chemical potential is introduced which allows chemical potential to be expressed in an analogous manner to electrical volts thus allowing engineering intuition to be applied to biomolecular systems. Redox reactions, and their representation by half-reactions, are key components of biological systems which involve both electrical and chemical domains. A bond graph interpretation of redox reactions is given which combines bond graphs with the Faraday-equivalent chemical potential. This approach is particularly relevant when the biomolecular system implements chemoelectrical transduction - for example chemiosmosis within the key metabolic pathway of mitochondria: oxidative phosphorylation. An alternative way of implementing computational modularity using bond graphs is introduced and used to give a physically based model of the mitochondrial electron transport chain To illustrate the overall approach, this model is analyzed using the Faraday-equivalent chemical potential approach and engineering intuition is used to guide affinity equalisation: a energy based analysis of the mitochondrial electron transport chain.

Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.

PubMed

Doostparast Torshizi, Abolfazl; Petzold, Linda R

2018-01-01

Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels. In this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data. The process of graph construction from biological pathways is based on detecting condition-responsive genes, where 3 sets of genes are finally extracted: all condition responsive genes, high-frequency condition-responsive genes, and P-value-filtered genes. The proposed approach is applied to ovarian cancer data downloaded from the Human Genome Atlas. Extensive numerical experiments demonstrate superior performance of the proposed approach compared to other state-of-the-art algorithms, including the latest graph-based classification techniques. Simulation results demonstrate that integrating various data types enhances classification performance and leads to a better understanding of interrelations between diverse omics data types. The proposed approach outperforms many of the state-of-the-art data integration algorithms. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Determining the Basis of Homodesmotic Reactions of Cyclic Organic Compounds by Means of Graph Theory

NASA Astrophysics Data System (ADS)

Khursan, S. L.; Ismagilova, A. S.; Akhmetyanova, A. I.

2018-07-01

Comparative calculations based on the use of a homodesmotic reaction (HDR)—an isodesmic process with the additional requirement for group balance—is used to analyze the thermochemical characteristics of cyclic organic compounds exemplified by bicyclo[2.1.0]pentene-2. To avoid confusion in selecting HDRs, an algorithm is developed for determining the HDR basis, i.e., the set of all possible independent homodesmotic reactions. The algorithm for constructing the set of HDRs is based on an analysis and transformations of the bond graph of groups for the investigated chemical compound. The use of graph theory allows us to automate the procedure for deriving the basis of homodesmotic reactions, and to obtain a visual geometric interpretation of the basis, which is important for subsequent physicochemical analysis. The energetics of bicyclo[2.1.0]pentene-2 is investigated using the proposed approach, and the independent basis of HDRs is found to include 19 formal transformations. Standard enthalpies for the test compound and the participants of homodesmotic reactions are calculated using the G3 composite approach. Thermochemical analysis of the obtained data allows us to determine the standard enthalpy of formation of the bicycle (Δf H° = 336.4 kJ/mol) and value Δf H° of a number of cyclic and acyclic alkenes and alkadienes that are products of theoretical decomposition of the test compound. The proposed method is shown to be extremely effective in analyzing the effects of nonbonded interactions in the structure of organic molecules. The ring strain energy of the bicycle is calculated or the test compound: E S = 295.2± 2.2 kJ/mol.

Automated visualization of rule-based models

PubMed Central

Tapia, Jose-Juan; Faeder, James R.

2017-01-01

Frameworks such as BioNetGen, Kappa and Simmune use “reaction rules” to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models. PMID:29131816

Compound analysis via graph kernels incorporating chirality.

PubMed

Brown, J B; Urata, Takashi; Tamura, Takeyuki; Arai, Midori A; Kawabata, Takeo; Akutsu, Tatsuya

2010-12-01

High accuracy is paramount when predicting biochemical characteristics using Quantitative Structural-Property Relationships (QSPRs). Although existing graph-theoretic kernel methods combined with machine learning techniques are efficient for QSPR model construction, they cannot distinguish topologically identical chiral compounds which often exhibit different biological characteristics. In this paper, we propose a new method that extends the recently developed tree pattern graph kernel to accommodate stereoisomers. We show that Support Vector Regression (SVR) with a chiral graph kernel is useful for target property prediction by demonstrating its application to a set of human vitamin D receptor ligands currently under consideration for their potential anti-cancer effects.

Designing a multiroute synthesis scheme in combinatorial chemistry.

PubMed

Akavia, Adi; Senderowitz, Hanoch; Lerner, Alon; Shamir, Ron

2004-01-01

Solid-phase mix-and-split combinatorial synthesis is often used to produce large arrays of compounds to be tested during the various stages of the drug development process. This method can be represented by a synthesis graph in which nodes correspond to grow operations and arcs to beads transferred among the different reaction vessels. In this work, we address the problem of designing such a graph which maximizes the number of produced target compounds (namely, compounds out of an input library of desired molecules), given constraints on the number of beads used for library synthesis and on the number of reaction vessels available for concurrent grow steps. We present a heuristic based on a discrete search for solving this problem, test our solution on several data sets, explore its behavior, and show that it achieves good performance.

The Fragment Network: A Chemistry Recommendation Engine Built Using a Graph Database.

PubMed

Hall, Richard J; Murray, Christopher W; Verdonk, Marcel L

2017-07-27

The hit validation stage of a fragment-based drug discovery campaign involves probing the SAR around one or more fragment hits. This often requires a search for similar compounds in a corporate collection or from commercial suppliers. The Fragment Network is a graph database that allows a user to efficiently search chemical space around a compound of interest. The result set is chemically intuitive, naturally grouped by substitution pattern and meaningfully sorted according to the number of observations of each transformation in medicinal chemistry databases. This paper describes the algorithms used to construct and search the Fragment Network and provides examples of how it may be used in a drug discovery context.

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

PubMed Central

Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

2013-01-01

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype. PMID:24204854

Metabolomics analysis: Finding out metabolic building blocks

PubMed Central

2017-01-01

In this paper we propose a new methodology for the analysis of metabolic networks. We use the notion of strongly connected components of a graph, called in this context metabolic building blocks. Every strongly connected component is contracted to a single node in such a way that the resulting graph is a directed acyclic graph, called a metabolic DAG, with a considerably reduced number of nodes. The property of being a directed acyclic graph brings out a background graph topology that reveals the connectivity of the metabolic network, as well as bridges, isolated nodes and cut nodes. Altogether, it becomes a key information for the discovery of functional metabolic relations. Our methodology has been applied to the glycolysis and the purine metabolic pathways for all organisms in the KEGG database, although it is general enough to work on any database. As expected, using the metabolic DAGs formalism, a considerable reduction on the size of the metabolic networks has been obtained, specially in the case of the purine pathway due to its relative larger size. As a proof of concept, from the information captured by a metabolic DAG and its corresponding metabolic building blocks, we obtain the core of the glycolysis pathway and the core of the purine metabolism pathway and detect some essential metabolic building blocks that reveal the key reactions in both pathways. Finally, the application of our methodology to the glycolysis pathway and the purine metabolism pathway reproduce the tree of life for the whole set of the organisms represented in the KEGG database which supports the utility of this research. PMID:28493998

Quantification of Graph Complexity Based on the Edge Weight Distribution Balance: Application to Brain Networks.

PubMed

Gomez-Pilar, Javier; Poza, Jesús; Bachiller, Alejandro; Gómez, Carlos; Núñez, Pablo; Lubeiro, Alba; Molina, Vicente; Hornero, Roberto

2018-02-01

The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC). This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs. The proposed complexity measure was designed to capture the interplay between two properties of a system: the 'information' (calculated by means of Shannon entropy) and the 'order' of the system (estimated by means of a disequilibrium measure). SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution. In this study, SGC was assessed using four synthetic graph datasets and a real dataset, formed by electroencephalographic (EEG) recordings from controls and schizophrenia patients. SGC was compared with graph density (GD), a classical measure used to evaluate graph complexity. Our results showed that SGC is invariant with respect to GD and independent of node degree distribution. Furthermore, its variation with graph size [Formula: see text] is close to zero for [Formula: see text]. Results from the real dataset showed an increment in the weight distribution balance during the cognitive processing for both controls and schizophrenia patients, although these changes are more relevant for controls. Our findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process. In addition, SGC results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

PubMed

Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

2014-01-01

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.

BootGraph: probabilistic fiber tractography using bootstrap algorithms and graph theory.

PubMed

Vorburger, Robert S; Reischauer, Carolin; Boesiger, Peter

2013-02-01

Bootstrap methods have recently been introduced to diffusion-weighted magnetic resonance imaging to estimate the measurement uncertainty of ensuing diffusion parameters directly from the acquired data without the necessity to assume a noise model. These methods have been previously combined with deterministic streamline tractography algorithms to allow for the assessment of connection probabilities in the human brain. Thereby, the local noise induced disturbance in the diffusion data is accumulated additively due to the incremental progression of streamline tractography algorithms. Graph based approaches have been proposed to overcome this drawback of streamline techniques. For this reason, the bootstrap method is in the present work incorporated into a graph setup to derive a new probabilistic fiber tractography method, called BootGraph. The acquired data set is thereby converted into a weighted, undirected graph by defining a vertex in each voxel and edges between adjacent vertices. By means of the cone of uncertainty, which is derived using the wild bootstrap, a weight is thereafter assigned to each edge. Two path finding algorithms are subsequently applied to derive connection probabilities. While the first algorithm is based on the shortest path approach, the second algorithm takes all existing paths between two vertices into consideration. Tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm. The BootGraph shows a very good performance in crossing situations with respect to false negatives and permits incorporating additional constraints, such as a curvature threshold. By inheriting the advantages of the bootstrap method and graph theory, the BootGraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of streamline tractography algorithms or the assumption of a noise distribution. Moreover, the BootGraph can be applied to common DTI data sets without further modifications and shows a high repeatability. Thus, it is very well suited for longitudinal studies and meta-studies based on DTI. Copyright © 2012 Elsevier Inc. All rights reserved.

TreeNetViz: revealing patterns of networks over tree structures.

PubMed

Gou, Liang; Zhang, Xiaolong Luke

2011-12-01

Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns. © 2011 IEEE

Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets.

PubMed

Salem, Saeed; Ozcaglar, Cagri

2014-01-01

Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression. We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links. The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links. Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules). Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways.

MIMO: an efficient tool for molecular interaction maps overlap

PubMed Central

2013-01-01

Background Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps. Results Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Conclusions MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways. PMID:23672344

Framework for Querying and Analysis of Evolving Graphs

ERIC Educational Resources Information Center

Moffitt, Vera Zaychik

2017-01-01

Graph representations underlie many modern computer applications, capturing the structure of such diverse networks as the Internet, personal associations, roads, sensors, and metabolic pathways. While the static structure of graphs is a well-explored field, a new emphasis is being placed on understanding and representing the way these networks…

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS.

PubMed

Durmaz, Arda; Henderson, Tim A D; Brubaker, Douglas; Bebek, Gurkan

2017-01-01

Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies. In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups. These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples. Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

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.

Iterated reaction graphs: simulating complex Maillard reaction pathways.

PubMed

Patel, S; Rabone, J; Russell, S; Tissen, J; Klaffke, W

2001-01-01

This study investigates a new method of simulating a complex chemical system including feedback loops and parallel reactions. The practical purpose of this approach is to model the actual reactions that take place in the Maillard process, a set of food browning reactions, in sufficient detail to be able to predict the volatile composition of the Maillard products. The developed framework, called iterated reaction graphs, consists of two main elements: a soup of molecules and a reaction base of Maillard reactions. An iterative process loops through the reaction base, taking reactants from and feeding products back to the soup. This produces a reaction graph, with molecules as nodes and reactions as arcs. The iterated reaction graph is updated and validated by comparing output with the main products found by classical gas-chromatographic/mass spectrometric analysis. To ensure a realistic output and convergence to desired volatiles only, the approach contains a number of novel elements: rate kinetics are treated as reaction probabilities; only a subset of the true chemistry is modeled; and the reactions are blocked into groups.

Visualization and recommendation of large image collections toward effective sensemaking

NASA Astrophysics Data System (ADS)

Gu, Yi; Wang, Chaoli; Nemiroff, Robert; Kao, David; Parra, Denis

2016-03-01

In our daily lives, images are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image collections and their associated text information. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords but also supports detailed comparison for understanding and intuitive guidance for navigation. The visual exploration of iGraph is further enhanced with the implementation of bubble sets to highlight group memberships of nodes, suggestion of abnormal keywords or time periods based on text outlier detection, and comparison of four different recommendation solutions. For performance speedup, multiple graphics processing units and central processing units are utilized for processing and visualization in parallel. We experiment with two image collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

Metabolomic Analysis and Visualization Engine for LC–MS Data

PubMed Central

Melamud, Eugene; Vastag, Livia; Rabinowitz, Joshua D.

2017-01-01

Metabolomic analysis by liquid chromatography–high-resolution mass spectrometry results in data sets with thousands of features arising from metabolites, fragments, isotopes, and adducts. Here we describe a software package, Metabolomic Analysis and Visualization ENgine (MAVEN), designed for efficient interactive analysis of LC–MS data, including in the presence of isotope labeling. The software contains tools for all aspects of the data analysis process, from feature extraction to pathway-based graphical data display. To facilitate data validation, a machine learning algorithm automatically assesses peak quality. Users interact with raw data primarily in the form of extracted ion chromatograms, which are displayed with overlaid circles indicating peak quality, and bar graphs of peak intensities for both unlabeled and isotope-labeled metabolite forms. Click-based navigation leads to additional information, such as raw data for specific isotopic forms or for metabolites changing significantly between conditions. Fast data processing algorithms result in nearly delay-free browsing. Drop-down menus provide tools for the overlay of data onto pathway maps. These tools enable animating series of pathway graphs, e.g., to show propagation of labeled forms through a metabolic network. MAVEN is released under an open source license at http://maven.princeton.edu. PMID:21049934

[A retrieval method of drug molecules based on graph collapsing].

PubMed

Qu, J W; Lv, X Q; Liu, Z M; Liao, Y; Sun, P H; Wang, B; Tang, Z

2018-04-18

To establish a compact and efficient hypergraph representation and a graph-similarity-based retrieval method of molecules to achieve effective and efficient medicine information retrieval. Chemical structural formula (CSF) was a primary search target as a unique and precise identifier for each compound at the molecular level in the research field of medicine information retrieval. To retrieve medicine information effectively and efficiently, a complete workflow of the graph-based CSF retrieval system was introduced. This system accepted the photos taken from smartphones and the sketches drawn on tablet personal computers as CSF inputs, and formalized the CSFs with the corresponding graphs. Then this paper proposed a compact and efficient hypergraph representation for molecules on the basis of analyzing factors that directly affected the efficiency of graph matching. According to the characteristics of CSFs, a hierarchical collapsing method combining graph isomorphism and frequent subgraph mining was adopted. There was yet a fundamental challenge, subgraph overlapping during the collapsing procedure, which hindered the method from establishing the correct compact hypergraph of an original CSF graph. Therefore, a graph-isomorphism-based algorithm was proposed to select dominant acyclic subgraphs on the basis of overlapping analysis. Finally, the spatial similarity among graphical CSFs was evaluated by multi-dimensional measures of similarity. To evaluate the performance of the proposed method, the proposed system was firstly compared with Wikipedia Chemical Structure Explorer (WCSE), the state-of-the-art system that allowed CSF similarity searching within Wikipedia molecules dataset, on retrieval accuracy. The system achieved higher values on mean average precision, discounted cumulative gain, rank-biased precision, and expected reciprocal rank than WCSE from the top-2 to the top-10 retrieved results. Specifically, the system achieved 10%, 1.41, 6.42%, and 1.32% higher than WCSE on these metrics for top-10 retrieval results, respectively. Moreover, several retrieval cases were presented to intuitively compare with WCSE. The results of the above comparative study demonstrated that the proposed method outperformed the existing method with regard to accuracy and effectiveness. This paper proposes a graph-similarity-based retrieval approach for medicine information. To obtain satisfactory retrieval results, an isomorphism-based algorithm is proposed for dominant subgraph selection based on the subgraph overlapping analysis, as well as an effective and efficient hypergraph representation of molecules. Experiment results demonstrate the effectiveness of the proposed approach.

Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach.

PubMed

Peng, Huiming; Peng, Tao; Wen, Jianguo; Engler, David A; Matsunami, Risë K; Su, Jing; Zhang, Le; Chang, Chung-Che Jeff; Zhou, Xiaobo

2014-07-01

p38 mitogen-activated protein kinase activation plays an important role in resistance to chemotherapeutic cytotoxic drugs in treating multiple myeloma (MM). However, how the p38 mitogen-activated protein kinase signaling pathway is involved in drug resistance, in particular the roles that the various p38 isoforms play, remains largely unknown. To explore the underlying mechanisms, we developed a novel systems biology approach by integrating liquid chromatography-mass spectrometry and reverse phase protein array data from human MM cell lines with computational pathway models in which the unknown parameters were inferred using a proposed novel algorithm called modularized factor graph. New mechanisms predicted by our models suggest that combined activation of various p38 isoforms may result in drug resistance in MM via regulating the related pathways including extracellular signal-regulated kinase (ERK) pathway and NFкB pathway. ERK pathway regulating cell growth is synergistically regulated by p38δ isoform, whereas nuclear factor kappa B (NFкB) pathway regulating cell apoptosis is synergistically regulated by p38α isoform. This finding that p38δ isoform promotes the phosphorylation of ERK1/2 in MM cells treated with bortezomib was validated by western blotting. Based on the predicted mechanisms, we further screened drug combinations in silico and found that a promising drug combination targeting ERK1/2 and NFκB might reduce the effects of drug resistance in MM cells. This study provides a framework of a systems biology approach to studying drug resistance and drug combination selection. RPPA experimental Data and Matlab source codes of modularized factor graph for parameter estimation are freely available online at http://ctsb.is.wfubmc.edu/publications/modularized-factor-graph.php. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Graph theory enables drug repurposing--how a mathematical model can drive the discovery of hidden mechanisms of action.

PubMed

Gramatica, Ruggero; Di Matteo, T; Giorgetti, Stefano; Barbiani, Massimo; Bevec, Dorian; Aste, Tomaso

2014-01-01

We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.

A graph-based approach to construct target-focused libraries for virtual screening.

PubMed

Naderi, Misagh; Alvin, Chris; Ding, Yun; Mukhopadhyay, Supratik; Brylinski, Michal

2016-01-01

Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family. Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns. We conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated. eSynth can successfully reconstruct chemically feasible molecules from molecular fragments. Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis.Graphical abstractAssuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. Here, we developed eSynth, an automated method to synthesize new compounds by reconnecting these building blocks following the connectivity patterns via an exhaustive graph-based search algorithm. eSynth opens up a possibility to rapidly construct virtual screening libraries for targeted drug discovery.

Identification of metabolic pathways using pathfinding approaches: a systematic review.

PubMed

Abd Algfoor, Zeyad; Shahrizal Sunar, Mohd; Abdullah, Afnizanfaizal; Kolivand, Hoshang

2017-03-01

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Modeling flow and transport in fracture networks using graphs

NASA Astrophysics Data System (ADS)

Karra, S.; O'Malley, D.; Hyman, J. D.; Viswanathan, H. S.; Srinivasan, G.

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O (104) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.

Modeling flow and transport in fracture networks using graphs.

PubMed

Karra, S; O'Malley, D; Hyman, J D; Viswanathan, H S; Srinivasan, G

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O(10^{4}) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.

Modeling flow and transport in fracture networks using graphs

DOE PAGES

Karra, S.; O'Malley, D.; Hyman, J. D.; ...

2018-03-09

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizationsmore » of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. In conclusion, the good accuracy and the low computational cost, with O(10 4) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.« less

Modeling flow and transport in fracture networks using graphs

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

Karra, S.; O'Malley, D.; Hyman, J. D.

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizationsmore » of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. In conclusion, the good accuracy and the low computational cost, with O(10 4) times lower times than the DFN, makes the graph algorithm an ideal technique to incorporate in uncertainty quantification methods.« less

Quantitative structure-retention relationships for gas chromatographic retention indices of alkylbenzenes with molecular graph descriptors.

PubMed

Ivanciuc, O; Ivanciuc, T; Klein, D J; Seitz, W A; Balaban, A T

2001-02-01

Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the connection between the molecular structure and the chromatographic retention indices of organic compounds, allowing the prediction of retention indices of novel, not yet synthesized compounds, solely from their structural descriptors. Using multiple linear regression, QSRR models for the gas chromatographic Kováts retention indices of 129 alkylbenzenes are generated using molecular graph descriptors. The correlational ability of structural descriptors computed from 10 molecular matrices is investigated, showing that the novel reciprocal matrices give numerical indices with improved correlational ability. A QSRR equation with 5 graph descriptors gives the best calibration and prediction results, demonstrating the usefulness of the molecular graph descriptors in modeling chromatographic retention parameters. The sequential orthogonalization of descriptors suggests simpler QSRR models by eliminating redundant structural information.

Matched signal detection on graphs: Theory and application to brain imaging data classification.

PubMed

Hu, Chenhui; Sepulcre, Jorge; Johnson, Keith A; Fakhri, Georges E; Lu, Yue M; Li, Quanzheng

2016-01-15

Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data. Copyright © 2015. Published by Elsevier Inc.

Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets

PubMed Central

2014-01-01

Background Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression. Results We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links. The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links. Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules). Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways. PMID:25221624

BFL: a node and edge betweenness based fast layout algorithm for large scale networks

PubMed Central

Hashimoto, Tatsunori B; Nagasaki, Masao; Kojima, Kaname; Miyano, Satoru

2009-01-01

Background Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements. Results To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths. Conclusion Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer. PMID:19146673

Historical contingency in fluviokarst landscape evolution

NASA Astrophysics Data System (ADS)

Phillips, Jonathan D.

2018-02-01

Lateral and vertical erosion at meander bends in the Kentucky River gorge area has created a series of strath terraces on the interior of incised meander bends. These represent a chronosequence of fluviokarst landscape evolution from the youngest valley side transition zone near the valley bottom to the oldest upland surface. This five-part chronosequence (not including the active river channel and floodplain) was analyzed in terms of the landforms that occur at each stage or surface. These include dolines, uvalas, karst valleys, pocket valleys, unincised channels, incised channels, and cliffs (smaller features such as swallets and shafts also occur). Landform coincidence analysis shows higher coincidence indices (CI) than would be expected based on an idealized chronosequence. CI values indicate genetic relationships (common causality) among some landforms and unexpected persistence of some features on older surfaces. The idealized and two observed chronosequences were also represented as graphs and analyzed using algebraic graph theory. The two field sites yielded graphs more complex and with less historical contingency than the idealized sequence. Indeed, some of the spectral graph measures for the field sites more closely approximate a purely hypothetical no-historical-contingency benchmark graph. The deviations of observations from the idealized expectations, and the high levels of graph complexity both point to potential transitions among landform types as being the dominant phenomenon, rather than canalization along a particular evolutionary pathway. As the base level of both the fluvial and karst landforms is lowered as the meanders expand, both fluvial and karst denudation are rejuvenated, and landform transitions remain active.

Cyclone: java-based querying and computing with Pathway/Genome databases.

PubMed

Le Fèvre, François; Smidtas, Serge; Schächter, Vincent

2007-05-15

Cyclone aims at facilitating the use of BioCyc, a collection of Pathway/Genome Databases (PGDBs). Cyclone provides a fully extensible Java Object API to analyze and visualize these data. Cyclone can read and write PGDBs, and can write its own data in the CycloneML format. This format is automatically generated from the BioCyc ontology by Cyclone itself, ensuring continued compatibility. Cyclone objects can also be stored in a relational database CycloneDB. Queries can be written in SQL, and in an intuitive and concise object-oriented query language, Hibernate Query Language (HQL). In addition, Cyclone interfaces easily with Java software including the Eclipse IDE for HQL edition, the Jung API for graph algorithms or Cytoscape for graph visualization. Cyclone is freely available under an open source license at: http://sourceforge.net/projects/nemo-cyclone. For download and installation instructions, tutorials, use cases and examples, see http://nemo-cyclone.sourceforge.net.

Building pathway graphs from BioPAX data in R.

PubMed

Benis, Nirupama; Schokker, Dirkjan; Kramer, Frank; Smits, Mari A; Suarez-Diez, Maria

2016-01-01

Biological pathways are increasingly available in the BioPAX format which uses an RDF model for data storage. One can retrieve the information in this data model in the scripting language R using the package rBiopaxParser , which converts the BioPAX format to one readable in R. It also has a function to build a regulatory network from the pathway information. Here we describe an extension of this function. The new function allows the user to build graphs of entire pathways, including regulated as well as non-regulated elements, and therefore provides a maximum of information. This function is available as part of the rBiopaxParser distribution from Bioconductor.

The integrated effect of moderate exercise on coronary heart disease.

PubMed

Mathews, Marc J; Mathews, Edward H; Mathews, George E

Moderate exercise is associated with a lower risk for coronary heart disease (CHD). A suitable integrated model of the CHD pathogenetic pathways relevant to moderate exercise may help to elucidate this association. Such a model is currently not available in the literature. An integrated model of CHD was developed and used to investigate pathogenetic pathways of importance between exercise and CHD. Using biomarker relative-risk data, the pathogenetic effects are representable as measurable effects based on changes in biomarkers. The integrated model provides insight into higherorder interactions underlying the associations between CHD and moderate exercise. A novel 'connection graph' was developed, which simplifies these interactions. It quantitatively illustrates the relationship between moderate exercise and various serological biomarkers of CHD. The connection graph of moderate exercise elucidates all the possible integrated actions through which risk reduction may occur. An integrated model of CHD provides a summary of the effects of moderate exercise on CHD. It also shows the importance of each CHD pathway that moderate exercise influences. The CHD risk-reducing effects of exercise appear to be primarily driven by decreased inflammation and altered metabolism.

Signalling Network Construction for Modelling Plant Defence Response

PubMed Central

Miljkovic, Dragana; Stare, Tjaša; Mozetič, Igor; Podpečan, Vid; Petek, Marko; Witek, Kamil; Dermastia, Marina; Lavrač, Nada; Gruden, Kristina

2012-01-01

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for modelling other biological systems, given that an adequate vocabulary is provided. PMID:23272172

Graph theoretical model of a sensorimotor connectome in zebrafish.

PubMed

Stobb, Michael; Peterson, Joshua M; Mazzag, Borbala; Gahtan, Ethan

2012-01-01

Mapping the detailed connectivity patterns (connectomes) of neural circuits is a central goal of neuroscience. The best quantitative approach to analyzing connectome data is still unclear but graph theory has been used with success. We present a graph theoretical model of the posterior lateral line sensorimotor pathway in zebrafish. The model includes 2,616 neurons and 167,114 synaptic connections. Model neurons represent known cell types in zebrafish larvae, and connections were set stochastically following rules based on biological literature. Thus, our model is a uniquely detailed computational representation of a vertebrate connectome. The connectome has low overall connection density, with 2.45% of all possible connections, a value within the physiological range. We used graph theoretical tools to compare the zebrafish connectome graph to small-world, random and structured random graphs of the same size. For each type of graph, 100 randomly generated instantiations were considered. Degree distribution (the number of connections per neuron) varied more in the zebrafish graph than in same size graphs with less biological detail. There was high local clustering and a short average path length between nodes, implying a small-world structure similar to other neural connectomes and complex networks. The graph was found not to be scale-free, in agreement with some other neural connectomes. An experimental lesion was performed that targeted three model brain neurons, including the Mauthner neuron, known to control fast escape turns. The lesion decreased the number of short paths between sensory and motor neurons analogous to the behavioral effects of the same lesion in zebrafish. This model is expandable and can be used to organize and interpret a growing database of information on the zebrafish connectome.

Radiation-resistant, amorphous, all-aromatic poly(arylene ether sulfones) - Synthesis, physical behavior, and degradation characteristics

NASA Technical Reports Server (NTRS)

Lewis, D. A.; O'Donnell, James H.; Hedrick, J. L.; Ward, T. C.; Mcgrath, J. E.

1989-01-01

The effects of Co-60 gamma radiation on a series of poly(arylene ether sulfones) prepared by nucleophilic activated aromatic substitution are investigated experimentally. The preparation of the test compounds is described, and the test results are presented in extensive tables and graphs. Radiation-induced degradation, as measured by SO2 production, was found to be lowest in compounds based on biphenol rather than bisphenol A; these findings were also well correlated with ultimate-tensile-strain measurements.

Application of kernel functions for accurate similarity search in large chemical databases.

PubMed

Wang, Xiaohong; Huan, Jun; Smalter, Aaron; Lushington, Gerald H

2010-04-29

Similarity search in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep. Efficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

PubMed

Young-Rae Cho; Yanan Xin; Speegle, Greg

2015-01-01

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction.

PubMed

Dhanasekaran, A Ranjitha; Pearson, Jon L; Ganesan, Balasubramanian; Weimer, Bart C

2015-02-25

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism's genome as a database restricts metabolite identification to only those compounds that the organism can produce. To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment's MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis. Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

Efficient searching and annotation of metabolic networks using chemical similarity

PubMed Central

Pertusi, Dante A.; Stine, Andrew E.; Broadbelt, Linda J.; Tyo, Keith E.J.

2015-01-01

Motivation: The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods—SimIndex (SI) and SimZyme—which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers–Waterman type pathway search algorithm for further paring down pertinent networks. Results: Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 105-fold. Subsequent Byers–Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Availability and implementation: Python files will be available for download at http://tyolab.northwestern.edu/tools/. Contact: k-tyo@northwestern.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25417203

Efficient searching and annotation of metabolic networks using chemical similarity.

PubMed

Pertusi, Dante A; Stine, Andrew E; Broadbelt, Linda J; Tyo, Keith E J

2015-04-01

The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks. Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Python files will be available for download at http://tyolab.northwestern.edu/tools/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

User-centered evaluation of Arizona BioPathway: an information extraction, integration, and visualization system.

PubMed

Quiñones, Karin D; Su, Hua; Marshall, Byron; Eggers, Shauna; Chen, Hsinchun

2007-09-01

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This paper presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature-viewing method. Relation aggregation significantly contributes to knowledge-acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multiview, relation-based interface that supports user-controlled exploration of pathway information across multiple granularities.

Semantics based approach for analyzing disease-target associations.

PubMed

Kaalia, Rama; Ghosh, Indira

2016-08-01

A complex disease is caused by heterogeneous biological interactions between genes and their products along with the influence of environmental factors. There have been many attempts for understanding the cause of these diseases using experimental, statistical and computational methods. In the present work the objective is to address the challenge of representation and integration of information from heterogeneous biomedical aspects of a complex disease using semantics based approach. Semantic web technology is used to design Disease Association Ontology (DAO-db) for representation and integration of disease associated information with diabetes as the case study. The functional associations of disease genes are integrated using RDF graphs of DAO-db. Three semantic web based scoring algorithms (PageRank, HITS (Hyperlink Induced Topic Search) and HITS with semantic weights) are used to score the gene nodes on the basis of their functional interactions in the graph. Disease Association Ontology for Diabetes (DAO-db) provides a standard ontology-driven platform for describing genes, proteins, pathways involved in diabetes and for integrating functional associations from various interaction levels (gene-disease, gene-pathway, gene-function, gene-cellular component and protein-protein interactions). An automatic instance loader module is also developed in present work that helps in adding instances to DAO-db on a large scale. Our ontology provides a framework for querying and analyzing the disease associated information in the form of RDF graphs. The above developed methodology is used to predict novel potential targets involved in diabetes disease from the long list of loose (statistically associated) gene-disease associations. Copyright © 2016 Elsevier Inc. All rights reserved.

Improving integrative searching of systems chemical biology data using semantic annotation.

PubMed

Chen, Bin; Ding, Ying; Wild, David J

2012-03-08

Systems chemical biology and chemogenomics are considered critical, integrative disciplines in modern biomedical research, but require data mining of large, integrated, heterogeneous datasets from chemistry and biology. We previously developed an RDF-based resource called Chem2Bio2RDF that enabled querying of such data using the SPARQL query language. Whilst this work has proved useful in its own right as one of the first major resources in these disciplines, its utility could be greatly improved by the application of an ontology for annotation of the nodes and edges in the RDF graph, enabling a much richer range of semantic queries to be issued. We developed a generalized chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i) simplifies the process of building SPARQL queries, (ii) enables useful new kinds of queries on the data and (iii) makes possible intelligent reasoning and semantic graph mining in chemogenomics and systems chemical biology. Chem2Bio2OWL is available at http://chem2bio2rdf.org/owl. The document is available at http://chem2bio2owl.wikispaces.com.

Integrating publicly-available data to generate computationally ...

EPA Pesticide Factsheets

The adverse outcome pathway (AOP) framework provides a way of organizing knowledge related to the key biological events that result in a particular health outcome. For the majority of environmental chemicals, the availability of curated pathways characterizing potential toxicity is limited. Methods are needed to assimilate large amounts of available molecular data and quickly generate putative AOPs for further testing and use in hazard assessment. A graph-based workflow was used to facilitate the integration of multiple data types to generate computationally-predicted (cp) AOPs. Edges between graph entities were identified through direct experimental or literature information or computationally inferred using frequent itemset mining. Data from the TG-GATEs and ToxCast programs were used to channel large-scale toxicogenomics information into a cpAOP network (cpAOPnet) of over 20,000 relationships describing connections between chemical treatments, phenotypes, and perturbed pathways measured by differential gene expression and high-throughput screening targets. Sub-networks of cpAOPs for a reference chemical (carbon tetrachloride, CCl4) and outcome (hepatic steatosis) were extracted using the network topology. Comparison of the cpAOP subnetworks to published mechanistic descriptions for both CCl4 toxicity and hepatic steatosis demonstrate that computational approaches can be used to replicate manually curated AOPs and identify pathway targets that lack genomic mar

Transcriptomic analysis in the developing zebrafish embryo after compound exposure: Individual gene expression and pathway regulation

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

Hermsen, Sanne A.B., E-mail: Sanne.Hermsen@rivm.nl; Department of Toxicogenomics, Maastricht University, P.O. Box 616, 6200 MD, Maastricht; Institute for Risk Assessment Sciences

2013-10-01

The zebrafish embryotoxicity test is a promising alternative assay for developmental toxicity. Classically, morphological assessment of the embryos is applied to evaluate the effects of compound exposure. However, by applying differential gene expression analysis the sensitivity and predictability of the test may be increased. For defining gene expression signatures of developmental toxicity, we explored the possibility of using gene expression signatures of compound exposures based on commonly expressed individual genes as well as based on regulated gene pathways. Four developmental toxic compounds were tested in concentration-response design, caffeine, carbamazepine, retinoic acid and valproic acid, and two non-embryotoxic compounds, D-mannitol andmore » saccharin, were included. With transcriptomic analyses we were able to identify commonly expressed genes, which were mostly development related, after exposure to the embryotoxicants. We also identified gene pathways regulated by the embryotoxicants, suggestive of their modes of action. Furthermore, whereas pathways may be regulated by all compounds, individual gene expression within these pathways can differ for each compound. Overall, the present study suggests that the use of individual gene expression signatures as well as pathway regulation may be useful starting points for defining gene biomarkers for predicting embryotoxicity. - Highlights: • The zebrafish embryotoxicity test in combination with transcriptomics was used. • We explored two approaches of defining gene biomarkers for developmental toxicity. • Four compounds in concentration-response design were tested. • We identified commonly expressed individual genes as well as regulated gene pathways. • Both approaches seem suitable starting points for defining gene biomarkers.« less

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

2008-07-01

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

Using MathCAD to Teach One-Dimensional Graphs

ERIC Educational Resources Information Center

Yushau, B.

2004-01-01

Topics such as linear and nonlinear equations and inequalities, compound inequalities, linear and nonlinear absolute value equations and inequalities, rational equations and inequality are commonly found in college algebra and precalculus textbooks. What is common about these topics is the fact that their solutions and graphs lie in the real line…

An algorithm for automated layout of process description maps drawn in SBGN.

PubMed

Genc, Begum; Dogrusoz, Ugur

2016-01-01

Evolving technology has increased the focus on genomics. The combination of today's advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

An algorithm for automated layout of process description maps drawn in SBGN

PubMed Central

Genc, Begum; Dogrusoz, Ugur

2016-01-01

Motivation: Evolving technology has increased the focus on genomics. The combination of today’s advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. Results: We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. Availability and implementation: An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). Contact: ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26363029

SING: Subgraph search In Non-homogeneous Graphs

PubMed Central

2010-01-01

Background Finding the subgraphs of a graph database that are isomorphic to a given query graph has practical applications in several fields, from cheminformatics to image understanding. Since subgraph isomorphism is a computationally hard problem, indexing techniques have been intensively exploited to speed up the process. Such systems filter out those graphs which cannot contain the query, and apply a subgraph isomorphism algorithm to each residual candidate graph. The applicability of such systems is limited to databases of small graphs, because their filtering power degrades on large graphs. Results In this paper, SING (Subgraph search In Non-homogeneous Graphs), a novel indexing system able to cope with large graphs, is presented. The method uses the notion of feature, which can be a small subgraph, subtree or path. Each graph in the database is annotated with the set of all its features. The key point is to make use of feature locality information. This idea is used to both improve the filtering performance and speed up the subgraph isomorphism task. Conclusions Extensive tests on chemical compounds, biological networks and synthetic graphs show that the proposed system outperforms the most popular systems in query time over databases of medium and large graphs. Other specific tests show that the proposed system is effective for single large graphs. PMID:20170516

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction

PubMed Central

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Objective Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Methods Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Results Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Conclusions Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. PMID:25002459

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

PubMed

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

[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.

Multidimensional spectral load balancing

DOEpatents

Hendrickson, Bruce A.; Leland, Robert W.

1996-12-24

A method of and apparatus for graph partitioning involving the use of a plurality of eigenvectors of the Laplacian matrix of the graph of the problem for which load balancing is desired. The invention is particularly useful for optimizing parallel computer processing of a problem and for minimizing total pathway lengths of integrated circuits in the design stage.

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

Visualizing excipient composition and homogeneity of Compound Liquorice Tablets by near-infrared chemical imaging

NASA Astrophysics Data System (ADS)

Wu, Zhisheng; Tao, Ou; Cheng, Wei; Yu, Lu; Shi, Xinyuan; Qiao, Yanjiang

2012-02-01

This study demonstrated that near-infrared chemical imaging (NIR-CI) was a promising technology for visualizing the spatial distribution and homogeneity of Compound Liquorice Tablets. The starch distribution (indirectly, plant extraction) could be spatially determined using basic analysis of correlation between analytes (BACRA) method. The correlation coefficients between starch spectrum and spectrum of each sample were greater than 0.95. Depending on the accurate determination of starch distribution, a method to determine homogeneous distribution was proposed by histogram graph. The result demonstrated that starch distribution in sample 3 was relatively heterogeneous according to four statistical parameters. Furthermore, the agglomerates domain in each tablet was detected using score image layers of principal component analysis (PCA) method. Finally, a novel method named Standard Deviation of Macropixel Texture (SDMT) was introduced to detect agglomerates and heterogeneity based on binary image. Every binary image was divided into different sizes length of macropixel and the number of zero values in each macropixel was counted to calculate standard deviation. Additionally, a curve fitting graph was plotted on the relationship between standard deviation and the size length of macropixel. The result demonstrated the inter-tablet heterogeneity of both starch and total compounds distribution, simultaneously, the similarity of starch distribution and the inconsistency of total compounds distribution among intra-tablet were signified according to the value of slope and intercept parameters in the curve.

Causal discovery in the geosciences-Using synthetic data to learn how to interpret results

NASA Astrophysics Data System (ADS)

Ebert-Uphoff, Imme; Deng, Yi

2017-02-01

Causal discovery algorithms based on probabilistic graphical models have recently emerged in geoscience applications for the identification and visualization of dynamical processes. The key idea is to learn the structure of a graphical model from observed spatio-temporal data, thus finding pathways of interactions in the observed physical system. Studying those pathways allows geoscientists to learn subtle details about the underlying dynamical mechanisms governing our planet. Initial studies using this approach on real-world atmospheric data have shown great potential for scientific discovery. However, in these initial studies no ground truth was available, so that the resulting graphs have been evaluated only by whether a domain expert thinks they seemed physically plausible. The lack of ground truth is a typical problem when using causal discovery in the geosciences. Furthermore, while most of the connections found by this method match domain knowledge, we encountered one type of connection for which no explanation was found. To address both of these issues we developed a simulation framework that generates synthetic data of typical atmospheric processes (advection and diffusion). Applying the causal discovery algorithm to the synthetic data allowed us (1) to develop a better understanding of how these physical processes appear in the resulting connectivity graphs, and thus how to better interpret such connectivity graphs when obtained from real-world data; (2) to solve the mystery of the previously unexplained connections.

HBVPathDB: a database of HBV infection-related molecular interaction network.

PubMed

Zhang, Yi; Bo, Xiao-Chen; Yang, Jing; Wang, Sheng-Qi

2005-03-21

To describe molecules or genes interaction between hepatitis B viruses (HBV) and host, for understanding how virus' and host's genes and molecules are networked to form a biological system and for perceiving mechanism of HBV infection. The knowledge of HBV infection-related reactions was organized into various kinds of pathways with carefully drawn graphs in HBVPathDB. Pathway information is stored with relational database management system (DBMS), which is currently the most efficient way to manage large amounts of data and query is implemented with powerful Structured Query Language (SQL). The search engine is written using Personal Home Page (PHP) with SQL embedded and web retrieval interface is developed for searching with Hypertext Markup Language (HTML). We present the first version of HBVPathDB, which is a HBV infection-related molecular interaction network database composed of 306 pathways with 1 050 molecules involved. With carefully drawn graphs, pathway information stored in HBVPathDB can be browsed in an intuitive way. We develop an easy-to-use interface for flexible accesses to the details of database. Convenient software is implemented to query and browse the pathway information of HBVPathDB. Four search page layout options-category search, gene search, description search, unitized search-are supported by the search engine of the database. The database is freely available at http://www.bio-inf.net/HBVPathDB/HBV/. The conventional perspective HBVPathDB have already contained a considerable amount of pathway information with HBV infection related, which is suitable for in-depth analysis of molecular interaction network of virus and host. HBVPathDB integrates pathway data-sets with convenient software for query, browsing, visualization, that provides users more opportunity to identify regulatory key molecules as potential drug targets and to explore the possible mechanism of HBV infection based on gene expression datasets.

Mining relational paths in integrated biomedical data.

PubMed

He, Bing; Tang, Jie; Ding, Ying; Wang, Huijun; Sun, Yuyin; Shin, Jae Hong; Chen, Bin; Moorthy, Ganesh; Qiu, Judy; Desai, Pankaj; Wild, David J

2011-01-01

Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies.

Visualizing Ecosystem Energy Flow in Complex Food Web Networks: A Comparison of Three Alaskan Large Marine Ecosystems

NASA Astrophysics Data System (ADS)

Kearney, K.; Aydin, K.

2016-02-01

Oceanic food webs are often depicted as network graphs, with the major organisms or functional groups displayed as nodes and the fluxes of between them as the edges. However, the large number of nodes and edges and high connectance of many management-oriented food webs coupled with graph layout algorithms poorly-suited to certain desired characteristics of food web visualizations often lead to hopelessly tangled diagrams that convey little information other than, "It's complex." Here, I combine several new graph visualization techniques- including a new node layout alorithm based on a trophic similarity (quantification of shared predator and prey) and trophic level, divided edge bundling for edge routing, and intelligent automated placement of labels- to create a much clearer visualization of the important fluxes through a food web. The technique will be used to highlight the differences in energy flow within three Alaskan Large Marine Ecosystems (the Bering Sea, Gulf of Alaska, and Aleutian Islands) that include very similar functional groups but unique energy pathways.

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

Identification of the active compounds and significant pathways of yinchenhao decoction based on network pharmacology

PubMed Central

Huang, Jihan; Cheung, Fan; Tan, Hor-Yue; Hong, Ming; Wang, Ning; Yang, Juan; Feng, Yibin; Zheng, Qingshan

2017-01-01

Yinchenhao decoction (YCHD) is a traditional Chinese medicine formulation, which has been widely used for the treatment of jaundice for 2,000 years. Currently, YCHD is used to treat various liver disorders and metabolic diseases, however its chemical/pharmacologic profiles remain to be elucidated. The present study identified the active compounds and significant pathways of YCHD based on network pharmacology. All of the chemical ingredients of YCHD were retrieved from the Traditional Chinese Medicine Systems Pharmacology database. Absorption, distribution, metabolism and excretion screening with oral bioavailability (OB) screening, drug-likeness (DL) and intestinal epithelial permeability (Caco-2) evaluation were applied to discover the bioactive compounds in YCHD. Following this, target prediction, pathway identification and network construction were employed to clarify the mechanism of action of YCHD. Following OB screening, and evaluation of DL and Caco-2, 34 compounds in YCHD were identified as potential active ingredients, of which 30 compounds were associated with 217 protein targets. A total of 31 significant pathways were obtained by performing enrichment analyses of 217 proteins using the JEPETTO 3.x plugin, and 16 classes of gene-associated diseases were revealed by performing enrichment analyses using Database for Annotation, Visualization and Integrated Discovery v6.7. The present study identified potential active compounds and significant pathways in YCHD. In addition, the mechanism of action of YCHD in the treatment of various diseases through multiple pathways was clarified. PMID:28791364

All-Optical Implementation of the Ant Colony Optimization Algorithm

PubMed Central

Hu, Wenchao; Wu, Kan; Shum, Perry Ping; Zheludev, Nikolay I.; Soci, Cesare

2016-01-01

We report all-optical implementation of the optimization algorithm for the famous “ant colony” problem. Ant colonies progressively optimize pathway to food discovered by one of the ants through identifying the discovered route with volatile chemicals (pheromones) secreted on the way back from the food deposit. Mathematically this is an important example of graph optimization problem with dynamically changing parameters. Using an optical network with nonlinear waveguides to represent the graph and a feedback loop, we experimentally show that photons traveling through the network behave like ants that dynamically modify the environment to find the shortest pathway to any chosen point in the graph. This proof-of-principle demonstration illustrates how transient nonlinearity in the optical system can be exploited to tackle complex optimization problems directly, on the hardware level, which may be used for self-routing of optical signals in transparent communication networks and energy flow in photonic systems. PMID:27222098

Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.

PubMed

Miannay, Bertrand; Minvielle, Stéphane; Magrangeas, Florence; Guziolowski, Carito

2018-03-21

The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles.

Image-based compound profiling reveals a dual inhibitor of tyrosine kinase and microtubule polymerization.

PubMed

Tanabe, Kenji

2016-04-27

Small-molecule compounds are widely used as biological research tools and therapeutic drugs. Therefore, uncovering novel targets of these compounds should provide insights that are valuable in both basic and clinical studies. I developed a method for image-based compound profiling by quantitating the effects of compounds on signal transduction and vesicle trafficking of epidermal growth factor receptor (EGFR). Using six signal transduction molecules and two markers of vesicle trafficking, 570 image features were obtained and subjected to multivariate analysis. Fourteen compounds that affected EGFR or its pathways were classified into four clusters, based on their phenotypic features. Surprisingly, one EGFR inhibitor (CAS 879127-07-8) was classified into the same cluster as nocodazole, a microtubule depolymerizer. In fact, this compound directly depolymerized microtubules. These results indicate that CAS 879127-07-8 could be used as a chemical probe to investigate both the EGFR pathway and microtubule dynamics. The image-based multivariate analysis developed herein has potential as a powerful tool for discovering unexpected drug properties.

On Topological Indices of Certain Families of Nanostar Dendrimers.

PubMed

Husin, Mohamad Nazri; Hasni, Roslan; Arif, Nabeel Ezzulddin; Imran, Muhammad

2016-06-24

A topological index of graph G is a numerical parameter related to G which characterizes its molecular topology and is usually graph invariant. In the field of quantitative structure-activity (QSAR)/quantitative structure-activity structure-property (QSPR) research, theoretical properties of the chemical compounds and their molecular topological indices such as the Randić connectivity index, atom-bond connectivity (ABC) index and geometric-arithmetic (GA) index are used to predict the bioactivity of different chemical compounds. A dendrimer is an artificially manufactured or synthesized molecule built up from the branched units called monomers. In this paper, the fourth version of ABC index and the fifth version of GA index of certain families of nanostar dendrimers are investigated. We derive the analytical closed formulas for these families of nanostar dendrimers. The obtained results can be of use in molecular data mining, particularly in researching the uniqueness of tested (hyper-branched) molecular graphs.

JavaGenes: Evolving Graphs with Crossover

NASA Technical Reports Server (NTRS)

Globus, Al; Atsatt, Sean; Lawton, John; Wipke, Todd

2000-01-01

Genetic algorithms usually use string or tree representations. We have developed a novel crossover operator for a directed and undirected graph representation, and used this operator to evolve molecules and circuits. Unlike strings or trees, a single point in the representation cannot divide every possible graph into two parts, because graphs may contain cycles. Thus, the crossover operator is non-trivial. A steady-state, tournament selection genetic algorithm code (JavaGenes) was written to implement and test the graph crossover operator. All runs were executed by cycle-scavagging on networked workstations using the Condor batch processing system. The JavaGenes code has evolved pharmaceutical drug molecules and simple digital circuits. Results to date suggest that JavaGenes can evolve moderate sized drug molecules and very small circuits in reasonable time. The algorithm has greater difficulty with somewhat larger circuits, suggesting that directed graphs (circuits) are more difficult to evolve than undirected graphs (molecules), although necessary differences in the crossover operator may also explain the results. In principle, JavaGenes should be able to evolve other graph-representable systems, such as transportation networks, metabolic pathways, and computer networks. However, large graphs evolve significantly slower than smaller graphs, presumably because the space-of-all-graphs explodes combinatorially with graph size. Since the representation strongly affects genetic algorithm performance, adding graphs to the evolutionary programmer's bag-of-tricks should be beneficial. Also, since graph evolution operates directly on the phenotype, the genotype-phenotype translation step, common in genetic algorithm work, is eliminated.

PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization.

PubMed

Dogrusoz, U; Erson, E Z; Giral, E; Demir, E; Babur, O; Cetintas, A; Colak, R

2006-02-01

Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.

Comparing DNA damage-processing pathways by computer analysis of chromosome painting data.

PubMed

Levy, Dan; Vazquez, Mariel; Cornforth, Michael; Loucas, Bradford; Sachs, Rainer K; Arsuaga, Javier

2004-01-01

Chromosome aberrations are large-scale illegitimate rearrangements of the genome. They are indicative of DNA damage and informative about damage processing pathways. Despite extensive investigations over many years, the mechanisms underlying aberration formation remain controversial. New experimental assays such as multiplex fluorescent in situ hybridyzation (mFISH) allow combinatorial "painting" of chromosomes and are promising for elucidating aberration formation mechanisms. Recently observed mFISH aberration patterns are so complex that computer and graph-theoretical methods are needed for their full analysis. An important part of the analysis is decomposing a chromosome rearrangement process into "cycles." A cycle of order n, characterized formally by the cyclic graph with 2n vertices, indicates that n chromatin breaks take part in a single irreducible reaction. We here describe algorithms for computing cycle structures from experimentally observed or computer-simulated mFISH aberration patterns. We show that analyzing cycles quantitatively can distinguish between different aberration formation mechanisms. In particular, we show that homology-based mechanisms do not generate the large number of complex aberrations, involving higher-order cycles, observed in irradiated human lymphocytes.

Physiologically-based pharmacokinetic (PBPK) modeling of metabolic pathways of bromochloromethane

EPA Science Inventory

Bromochloromethane (BCM) is a volatile compound that if metabolized can lead to toxicity in different organs. Using a physiologically-based phannacokinetic model, we explore two hypotheses describing the metabolic pathways of BCM in rats: a two-pathway model exploiting both the e...

Single-subject structural networks with closed-form rotation invariant matching mprove power in developmental studies of the cortex.

PubMed

Kandel, Benjamin M; Wang, Danny J J; Gee, James C; Avants, Brian B

2014-01-01

Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns.

Using Graph Indices for the Analysis and Comparison of Chemical Datasets.

PubMed

Fourches, Denis; Tropsha, Alexander

2013-10-01

In cheminformatics, compounds are represented as points in multidimensional space of chemical descriptors. When all pairs of points found within certain distance threshold in the original high dimensional chemistry space are connected by distance-labeled edges, the resulting data structure can be defined as Dataset Graph (DG). We show that, similarly to the conventional description of organic molecules, many graph indices can be computed for DGs as well. We demonstrate that chemical datasets can be effectively characterized and compared by computing simple graph indices such as the average vertex degree or Randic connectivity index. This approach is used to characterize and quantify the similarity between different datasets or subsets of the same dataset (e.g., training, test, and external validation sets used in QSAR modeling). The freely available ADDAGRA program has been implemented to build and visualize DGs. The approach proposed and discussed in this report could be further explored and utilized for different cheminformatics applications such as dataset diversification by acquiring external compounds, dataset processing prior to QSAR modeling, or (dis)similarity modeling of multiple datasets studied in chemical genomics applications. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Models construction for acetone-butanol-ethanol fermentations with acetate/butyrate consecutively feeding by graph theory.

PubMed

Li, Zhigang; Shi, Zhongping; Li, Xin

2014-05-01

Several fermentations with consecutively feeding of acetate/butyrate were conducted in a 7 L fermentor and the results indicated that exogenous acetate/butyrate enhanced solvents productivities by 47.1% and 39.2% respectively, and changed butyrate/acetate ratios greatly. Then extracellular butyrate/acetate ratios were utilized for calculation of acids rates and the results revealed that acetate and butyrate formation pathways were almost blocked by corresponding acids feeding. In addition, models for acetate/butyrate feeding fermentations were constructed by graph theory based on calculation results and relevant reports. Solvents concentrations and butanol/acetone ratios of these fermentations were also calculated and the results of models calculation matched fermentation data accurately which demonstrated that models were constructed in a reasonable way. Copyright © 2014 Elsevier Ltd. All rights reserved.

Connectome imaging for mapping human brain pathways

PubMed Central

Shi, Y; Toga, A W

2017-01-01

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research. PMID:28461700

In silico database screening of potential targets and pathways of compounds contained in plants used for psoriasis vulgaris.

PubMed

May, Brian H; Deng, Shiqiang; Zhang, Anthony L; Lu, Chuanjian; Xue, Charlie C L

2015-09-01

Reviews and meta-analyses of clinical trials identified plants used as traditional medicines (TMs) that show promise for psoriasis. These include Rehmannia glutinosa, Camptotheca acuminata, Indigo naturalis and Salvia miltiorrhiza. Compounds contained in these TMs have shown activities of relevance to psoriasis in experimental models. To further investigate the likely mechanisms of action of the multiple compounds in these TMs, we undertook a computer-based in silico investigation of the proteins known to be regulated by these compounds and their associated biological pathways. The proteins reportedly regulated by compounds in these four TMs were identified using the HIT (Herbal Ingredients' Targets) database. The resultant data were entered into the PANTHER (Protein ANnotation THrough Evolutionary Relationship) database to identify the pathways in which the proteins could be involved. The study identified 237 compounds in the TMs and these retrieved 287 proteins from HIT. These proteins identified 59 pathways in PANTHER with most proteins being located in the Apoptosis, Angiogenesis, Inflammation mediated by chemokine and cytokine, Gonadotropin releasing hormone receptor, and/or Interleukin signaling pathways. All four TMs contained compounds that had regulating effects on Apoptosis regulator BAX, Apoptosis regulator Bcl-2, Caspase-3, Tumor necrosis factor (TNF) or Prostaglandin G/H synthase 2 (COX2). The main proteins and pathways are primarily related to inflammation, proliferation and angiogenesis which are all processes involved in psoriasis. Experimental studies have reported that certain compounds from these TMs can regulate the expression of proteins involved in each of these pathways.

PathwaySplice: An R package for unbiased pathway analysis of alternative splicing in RNA-Seq data.

PubMed

Yan, Aimin; Ban, Yuguang; Gao, Zhen; Chen, Xi; Wang, Lily

2018-04-24

Pathway analysis of alternative splicing would be biased without accounting for the different number of exons or junctions associated with each gene, because genes with higher number of exons or junctions are more likely to be included in the "significant" gene list in alternative splicing. We present PathwaySplice, an R package that (1) Performs pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene; (2) Visualizes selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression; (3) Supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets; (4) Identifies the significant genes driving pathway significance and (5) Organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph. https://bioconductor.org/packages/release/bioc/html/PathwaySplice.html. lily.wangg@gmail.com, xi.steven.chen@gmail.com.

A generalized graph-theoretical matrix of heterosystems and its application to the VMV procedure.

PubMed

Mozrzymas, Anna

2011-12-14

The extensions of generalized (molecular) graph-theoretical matrix and vector-matrix-vector procedure are considered. The elements of the generalized matrix are redefined in order to describe molecules containing heteroatoms and multiple bonds. The adjacency, distance, detour and reciprocal distance matrices of heterosystems, and corresponding vectors are derived from newly defined generalized graph matrix. The topological indices, which are most widely used in predicting physicochemical and biological properties/activities of various compounds, can be calculated from the new generalized vector-matrix-vector invariant. Copyright © 2011 Elsevier Ltd. All rights reserved.

Pathview Web: user friendly pathway visualization and data integration

PubMed Central

Pant, Gaurav; Bhavnasi, Yeshvant K.; Blanchard, Steven G.; Brouwer, Cory

2017-01-01

Abstract Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/. PMID:28482075

Alignment of Tractograms As Graph Matching.

PubMed

Olivetti, Emanuele; Sharmin, Nusrat; Avesani, Paolo

2016-01-01

The white matter pathways of the brain can be reconstructed as 3D polylines, called streamlines, through the analysis of diffusion magnetic resonance imaging (dMRI) data. The whole set of streamlines is called tractogram and represents the structural connectome of the brain. In multiple applications, like group-analysis, segmentation, or atlasing, tractograms of different subjects need to be aligned. Typically, this is done with registration methods, that transform the tractograms in order to increase their similarity. In contrast with transformation-based registration methods, in this work we propose the concept of tractogram correspondence, whose aim is to find which streamline of one tractogram corresponds to which streamline in another tractogram, i.e., a map from one tractogram to another. As a further contribution, we propose to use the relational information of each streamline, i.e., its distances from the other streamlines in its own tractogram, as the building block to define the optimal correspondence. We provide an operational procedure to find the optimal correspondence through a combinatorial optimization problem and we discuss its similarity to the graph matching problem. In this work, we propose to represent tractograms as graphs and we adopt a recent inexact sub-graph matching algorithm to approximate the solution of the tractogram correspondence problem. On tractograms generated from the Human Connectome Project dataset, we report experimental evidence that tractogram correspondence, implemented as graph matching, provides much better alignment than affine registration and comparable if not better results than non-linear registration of volumes.

Graphs, matrices, and the GraphBLAS: Seven good reasons

DOE PAGES

Kepner, Jeremy; Bader, David; Buluç, Aydın; ...

2015-01-01

The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implementmore » a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.« less

Reactome diagram viewer: data structures and strategies to boost performance.

PubMed

Fabregat, Antonio; Sidiropoulos, Konstantinos; Viteri, Guilherme; Marin-Garcia, Pablo; Ping, Peipei; Stein, Lincoln; D'Eustachio, Peter; Hermjakob, Henning

2018-04-01

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources. Reactome is available online at: https://reactome.org. The diagram viewer is part of the Reactome pathway browser (https://reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://reactome.org/dev/diagram/. The source code is freely available at: https://github.com/reactome-pwp/diagram. fabregat@ebi.ac.uk or hhe@ebi.ac.uk. Supplementary data are available at Bioinformatics online.

GBS 1.0

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

2010-09-30

The Umbra gbs (Graph-Based Search) library provides implementations of graph-based search/planning algorithms that can be applied to legacy graph data structures. Unlike some other graph algorithm libraries, this one does not require your graph class to inherit from a specific base class. Implementations of Dijkstra's Algorithm and A-Star search are included and can be used with graphs that are lazily-constructed.

A Weight-Adaptive Laplacian Embedding for Graph-Based Clustering.

PubMed

Cheng, De; Nie, Feiping; Sun, Jiande; Gong, Yihong

2017-07-01

Graph-based clustering methods perform clustering on a fixed input data graph. Thus such clustering results are sensitive to the particular graph construction. If this initial construction is of low quality, the resulting clustering may also be of low quality. We address this drawback by allowing the data graph itself to be adaptively adjusted in the clustering procedure. In particular, our proposed weight adaptive Laplacian (WAL) method learns a new data similarity matrix that can adaptively adjust the initial graph according to the similarity weight in the input data graph. We develop three versions of these methods based on the L2-norm, fuzzy entropy regularizer, and another exponential-based weight strategy, that yield three new graph-based clustering objectives. We derive optimization algorithms to solve these objectives. Experimental results on synthetic data sets and real-world benchmark data sets exhibit the effectiveness of these new graph-based clustering methods.

Adjusting protein graphs based on graph entropy.

PubMed

Peng, Sheng-Lung; Tsay, Yu-Wei

2014-01-01

Measuring protein structural similarity attempts to establish a relationship of equivalence between polymer structures based on their conformations. In several recent studies, researchers have explored protein-graph remodeling, instead of looking a minimum superimposition for pairwise proteins. When graphs are used to represent structured objects, the problem of measuring object similarity become one of computing the similarity between graphs. Graph theory provides an alternative perspective as well as efficiency. Once a protein graph has been created, its structural stability must be verified. Therefore, a criterion is needed to determine if a protein graph can be used for structural comparison. In this paper, we propose a measurement for protein graph remodeling based on graph entropy. We extend the concept of graph entropy to determine whether a graph is suitable for representing a protein. The experimental results suggest that when applied, graph entropy helps a conformational on protein graph modeling. Furthermore, it indirectly contributes to protein structural comparison if a protein graph is solid.

Adjusting protein graphs based on graph entropy

PubMed Central

2014-01-01

Measuring protein structural similarity attempts to establish a relationship of equivalence between polymer structures based on their conformations. In several recent studies, researchers have explored protein-graph remodeling, instead of looking a minimum superimposition for pairwise proteins. When graphs are used to represent structured objects, the problem of measuring object similarity become one of computing the similarity between graphs. Graph theory provides an alternative perspective as well as efficiency. Once a protein graph has been created, its structural stability must be verified. Therefore, a criterion is needed to determine if a protein graph can be used for structural comparison. In this paper, we propose a measurement for protein graph remodeling based on graph entropy. We extend the concept of graph entropy to determine whether a graph is suitable for representing a protein. The experimental results suggest that when applied, graph entropy helps a conformational on protein graph modeling. Furthermore, it indirectly contributes to protein structural comparison if a protein graph is solid. PMID:25474347

PhAST: pharmacophore alignment search tool.

PubMed

Hähnke, Volker; Hofmann, Bettina; Grgat, Tomislav; Proschak, Ewgenij; Steinhilber, Dieter; Schneider, Gisbert

2009-04-15

We present a ligand-based virtual screening technique (PhAST) for rapid hit and lead structure searching in large compound databases. Molecules are represented as strings encoding the distribution of pharmacophoric features on the molecular graph. In contrast to other text-based methods using SMILES strings, we introduce a new form of text representation that describes the pharmacophore of molecules. This string representation opens the opportunity for revealing functional similarity between molecules by sequence alignment techniques in analogy to homology searching in protein or nucleic acid sequence databases. We favorably compared PhAST with other current ligand-based virtual screening methods in a retrospective analysis using the BEDROC metric. In a prospective application, PhAST identified two novel inhibitors of 5-lipoxygenase product formation with minimal experimental effort. This outcome demonstrates the applicability of PhAST to drug discovery projects and provides an innovative concept of sequence-based compound screening with substantial scaffold hopping potential. 2008 Wiley Periodicals, Inc.

Strategies for engineering plant natural products: the iridoid-derived monoterpene indole alkaloids of Catharanthus roseus.

PubMed

O'Connor, Sarah E

2012-01-01

The manipulation of pathways to make unnatural variants of natural compounds, a process often termed combinatorial biosynthesis, has been robustly successful in prokaryotic systems. The development of approaches to generate new-to-nature compounds from plant-based pathways is, in comparison, much less advanced. Success will depend on the specific chemistry of the pathway, as well as on the suitability of the plant system for transformation and genetic manipulation. As plant pathways are elucidated, and can be heterologously expressed in hosts that are more amenable to genetic manipulation, biosynthetic production of new-to-nature compounds from plant pathways will become more widespread. In this chapter, some of the key strategies that have been developed for metabolic engineering of plant pathways, namely directed biosynthesis, mutasynthesis, and pathway incorporation of engineered enzymes are highlighted. The iridoid-derived monoterpene indole alkaloids from C. roseus, which are the focus of this chapter, provide an excellent system for developing these strategies. Copyright © 2012 Elsevier Inc. All rights reserved.

Network Analysis Tools: from biological networks to clusters and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

Disentangling the multigenic and pleiotropic nature of molecular function

PubMed Central

2015-01-01

Background Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. Results We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. Conclusions Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes. PMID:26678917

Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma.

PubMed

Xu, Song; Liu, Renwang; Da, Yurong

2018-06-05

This study compared tumor-related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma (LUAD) treatment. Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses were performed based on LUAD differentially expressed genes from The Cancer Genome Atlas (TCGA) project and genotype-tissue expression controls. These results were compared to various known compounds using the Connectivity Mapping dataset. The clinical significance of the hub genes identified by overlapping pathway enrichment analysis was further investigated using data mining from multiple sources. A drug-pathway network for LUAD was constructed, and molecular docking was carried out. After the integration of 57 LUAD-related pathways and 35 pathways affected by small molecules, five overlapping pathways were revealed. Among these five pathways, the p53 signaling pathway was the most significant, with CCNB1, CCNB2, CDK1, CDKN2A, and CHEK1 being identified as hub genes. The p53 signaling pathway is implicated as a risk factor for LUAD tumorigenesis and survival. A total of 88 molecules significantly inhibiting the five LUAD-related oncogenic pathways were involved in the LUAD drug-pathway network. Daunorubicin, mycophenolic acid, and pyrvinium could potentially target the hub gene CHEK1 directly. Our study highlights the critical pathways that should be targeted in the search for potential LUAD treatments, most importantly, the p53 signaling pathway. Some compounds, such as ciclopirox and AG-028671, may have potential roles for LUAD treatment but require further experimental verification. © 2018 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

Mathematical Justification of Expression-Based Pathway Activation Scoring (PAS).

PubMed

Aliper, Alexander M; Korzinkin, Michael B; Kuzmina, Natalia B; Zenin, Alexander A; Venkova, Larisa S; Smirnov, Philip Yu; Zhavoronkov, Alex A; Buzdin, Anton A; Borisov, Nikolay M

2017-01-01

Although modeling of activation kinetics for various cell signaling pathways has reached a high grade of sophistication and thoroughness, most such kinetic models still remain of rather limited practical value for biomedicine. Nevertheless, recent advancements have been made in application of signaling pathway science for real needs of prescription of the most effective drugs for individual patients. The methods for such prescription evaluate the degree of pathological changes in the signaling machinery based on two types of data: first, on the results of high-throughput gene expression profiling, and second, on the molecular pathway graphs that reflect interactions between the pathway members. For example, our algorithm OncoFinder evaluates the activation of molecular pathways on the basis of gene/protein expression data in the objects of the interest.Yet, the question of assessment of the relative importance for each gene product in a molecular pathway remains unclear unless one call for the methods of parameter sensitivity /stiffness analysis in the interactomic kinetic models of signaling pathway activation in terms of total concentrations of each gene product.Here we show two principal points: 1. First, the importance coefficients for each gene in pathways that were obtained using the extremely time- and labor-consuming stiffness analysis of full-scaled kinetic models generally differ from much easier-to-calculate expression-based pathway activation score (PAS) not more than by 30%, so the concept of PAS is kinetically justified. 2. Second, the use of pathway-based approach instead of distinct gene analysis, due to the law of large numbers, allows restoring the correlation between the similar samples that were examined using different transcriptome investigation techniques.

GraphSAW: a web-based system for graphical analysis of drug interactions and side effects using pharmaceutical and molecular data.

PubMed

Shoshi, Alban; Hoppe, Tobias; Kormeier, Benjamin; Ogultarhan, Venus; Hofestädt, Ralf

2015-02-28

Adverse drug reactions are one of the most common causes of death in industrialized Western countries. Nowadays, empirical data from clinical studies for the approval and monitoring of drugs and molecular databases is available. The integration of database information is a promising method for providing well-based knowledge to avoid adverse drug reactions. This paper presents our web-based decision support system GraphSAW which analyzes and evaluates drug interactions and side effects based on data from two commercial and two freely available molecular databases. The system is able to analyze single and combined drug-drug interactions, drug-molecule interactions as well as single and cumulative side effects. In addition, it allows exploring associative networks of drugs, molecules, metabolic pathways, and diseases in an intuitive way. The molecular medication analysis includes the capabilities of the upper features. A statistical evaluation of the integrated data and top 20 drugs concerning drug interactions and side effects is performed. The results of the data analysis give an overview of all theoretically possible drug interactions and side effects. The evaluation shows a mismatch between pharmaceutical and molecular databases. The concordance of drug interactions was about 12% and 9% of drug side effects. An application case with prescribed data of 11 patients is presented in order to demonstrate the functionality of the system under real conditions. For each patient at least two interactions occured in every medication and about 8% of total diseases were possibly induced by drug therapy. GraphSAW (http://tunicata.techfak.uni-bielefeld.de/graphsaw/) is meant to be a web-based system for health professionals and researchers. GraphSAW provides comprehensive drug-related knowledge and an improved medication analysis which may support efforts to reduce the risk of medication errors and numerous drastic side effects.

Graph-based normalization and whitening for non-linear data analysis.

PubMed

Aaron, Catherine

2006-01-01

In this paper we construct a graph-based normalization algorithm for non-linear data analysis. The principle of this algorithm is to get a spherical average neighborhood with unit radius. First we present a class of global dispersion measures used for "global normalization"; we then adapt these measures using a weighted graph to build a local normalization called "graph-based" normalization. Then we give details of the graph-based normalization algorithm and illustrate some results. In the second part we present a graph-based whitening algorithm built by analogy between the "global" and the "local" problem.

A novel amperometric biosensor based on banana peel (Musa cavendish) tissue homogenate for determination of phenolic compounds.

PubMed

Ozcan, Hakki Mevlut; Sagiroglu, Ayten

2010-08-01

In this study the biosensor was constructed by immobilizing tissue homogenate of banana peel onto a glassy carbon electrode surface. Effects of immobilization materials amounts, effects of pH, buffer concentration and temperature on biosensor response were studied. In addition, the detection ranges of 13 phenolic compounds were obtained with the help of the calibration graphs. Storage stability, repeatability of the biosensor, inhibitory effect and sample applications were also investigated. A typical calibration curve for the sensor revealed a linear range of 10-80 microM catechol. In reproducibility studies, variation coefficient and standard deviation were calculated as 2.69%, 1.44 x 10(-3) microM, respectively.

Dynamic Visualization of Co-expression in Systems Genetics Data

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

New, Joshua Ryan; Huang, Jian; Chesler, Elissa J

2008-01-01

Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These tools should allow biologists to develop an intuitive understanding of the structure of biologicalmore » networks and discover genes which reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized b-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.« less

Genome alignment with graph data structures: a comparison

PubMed Central

2014-01-01

Background Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference. Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment. Results We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures. Conclusion We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools. PMID:24712884

Preserving Differential Privacy in Degree-Correlation based Graph Generation

PubMed Central

Wang, Yue; Wu, Xintao

2014-01-01

Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we study the problem of enforcing edge differential privacy in graph generation. The idea is to enforce differential privacy on graph model parameters learned from the original network and then generate the graphs for releasing using the graph model with the private parameters. In particular, we develop a differential privacy preserving graph generator based on the dK-graph generation model. We first derive from the original graph various parameters (i.e., degree correlations) used in the dK-graph model, then enforce edge differential privacy on the learned parameters, and finally use the dK-graph model with the perturbed parameters to generate graphs. For the 2K-graph model, we enforce the edge differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We conduct experiments on four real networks and compare the performance of our private dK-graph models with the stochastic Kronecker graph generation model in terms of utility and privacy tradeoff. Empirical evaluations show the developed private dK-graph generation models significantly outperform the approach based on the stochastic Kronecker generation model. PMID:24723987

Information visualisation based on graph models

NASA Astrophysics Data System (ADS)

Kasyanov, V. N.; Kasyanova, E. V.

2013-05-01

Information visualisation is a key component of support tools for many applications in science and engineering. A graph is an abstract structure that is widely used to model information for its visualisation. In this paper, we consider practical and general graph formalism called hierarchical graphs and present the Higres and Visual Graph systems aimed at supporting information visualisation on the base of hierarchical graph models.

Molecular graph convolutions: moving beyond fingerprints.

PubMed

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

A Whole-Cell Phenotypic Screening Platform for Identifying Methylerythritol Phosphate Pathway-Selective Inhibitors as Novel Antibacterial Agents

PubMed Central

Johnson, L. Jeffrey

2012-01-01

Isoprenoid biosynthesis is essential for survival of all living organisms. More than 50,000 unique isoprenoids occur naturally, with each constructed from two simple five-carbon precursors: isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). Two pathways for the biosynthesis of IPP and DMAPP are found in nature. Humans exclusively use the mevalonate (MVA) pathway, while most bacteria, including all Gram-negative and many Gram-positive species, use the unrelated methylerythritol phosphate (MEP) pathway. Here we report the development of a novel, whole-cell phenotypic screening platform to identify compounds that selectively inhibit the MEP pathway. Strains of Salmonella enterica serovar Typhimurium were engineered to have separately inducible MEP (native) and MVA (nonnative) pathways. These strains, RMC26 and CT31-7d, were then used to differentiate MVA pathway- and MEP pathway-specific perturbation. Compounds that inhibit MEP pathway-dependent bacterial growth but leave MVA-dependent growth unaffected represent MEP pathway-selective antibacterials. This screening platform offers three significant results. First, the compound is antibacterial and is therefore cell permeant, enabling access to the intracellular target. Second, the compound inhibits one or more MEP pathway enzymes. Third, the MVA pathway is unaffected, suggesting selectivity for targeting the bacterial versus host pathway. The cell lines also display increased sensitivity to two reported MEP pathway-specific inhibitors, further biasing the platform toward inhibitors selective for the MEP pathway. We demonstrate development of a robust, high-throughput screening platform that combines phenotypic and target-based screening that can identify MEP pathway-selective antibacterials simply by monitoring optical density as the readout for cell growth/inhibition. PMID:22777049

Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection

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

Chen, Pin-Yu; Choudhury, Sutanay; Hero, Alfred

Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the centered graph adjacency matrix (graph Laplacian), we propose spectral decomposition approaches to graph PCA and graph dictionary learning that integrate multiple features, including graph walk statistics, centrality measures and graph distances to reference nodes. In this paper we propose a new PCA method for single graph analysis, called multi-centrality graph PCA (MC-GPCA), and a new dictionary learning method for ensembles ofmore » graphs, called multi-centrality graph dictionary learning (MC-GDL), both based on spectral decomposition of multi-centrality matrices. As an application to cyber intrusion detection, MC-GPCA can be an effective indicator of anomalous connectivity pattern and MC-GDL can provide discriminative basis for attack classification.« less

Fresh broad (Vicia faba) tissue homogenate-based biosensor for determination of phenolic compounds.

PubMed

Ozcan, Hakki Mevlut; Sagiroglu, Ayten

2014-08-01

In this study, a novel fresh broad (Vicia faba) tissue homogenate-based biosensor for determination of phenolic compounds was developed. The biosensor was constructed by immobilizing tissue homogenate of fresh broad (Vicia faba) on to glassy carbon electrode. For the stability of the biosensor, general immobilization techniques were used to secure the fresh broad tissue homogenate in gelatin-glutaraldehyde cross-linking matrix. In the optimization and characterization studies, the amount of fresh broad tissue homogenate and gelatin, glutaraldehyde percentage, optimum pH, optimum temperature and optimum buffer concentration, thermal stability, interference effects, linear range, storage stability, repeatability and sample applications (Wine, beer, fruit juices) were also investigated. Besides, the detection ranges of thirteen phenolic compounds were obtained with the help of the calibration graphs. A typical calibration curve for the sensor revealed a linear range of 5-60 μM catechol. In reproducibility studies, variation coefficient (CV) and standard deviation (SD) were calculated as 1.59%, 0.64×10(-3) μM, respectively.

Nrf2 and HSF-1 Pathway Activation via Hydroquinone-Based Proelectrophilic Small Molecules Is Regulated by Electrochemical Oxidation Potential

PubMed Central

Stalder, Romain; McKercher, Scott R.; Williamson, Robert E.; Roth, Gregory P.; Lipton, Stuart A.

2015-01-01

Activation of the Kelch-like ECH-associated protein 1/nuclear factor (erythroid-derived 2)-like 2 and heat-shock protein 90/heat-shock factor-1 signal-transduction pathways plays a central role in combatting cellular oxidative damage and related endoplasmic reticulum stress. Electrophilic compounds have been shown to be activators of these transcription-mediated responses through S-alkylation of specific regulatory proteins. Previously, we reported that a prototype compound (D1, a small molecule representing a proelectrophilic, para-hydroquinone species) exhibited neuroprotective action by activating both of these pathways. We hypothesized that the para-hydroquinone moiety was critical for this activation because it enhanced transcription of these neuroprotective pathways to a greater degree than that of the corresponding ortho-hydroquinone isomer. This notion was based on the differential oxidation potentials of the isomers for the transformation of the hydroquinone to the active, electrophilic quinone species. Here, to further test this hypothesis, we synthesized a pair of para- and ortho-hydroquinone-based proelectrophilic compounds and measured their redox potentials using analytical cyclic voltammetry. The redox potential was then compared with functional biological activity, and the para-hydroquinones demonstrated a superior neuroprotective profile. PMID:26243592

Nrf2 and HSF-1 Pathway Activation via Hydroquinone-Based Proelectrophilic Small Molecules is Regulated by Electrochemical Oxidation Potential.

PubMed

Satoh, Takumi; Stalder, Romain; McKercher, Scott R; Williamson, Robert E; Roth, Gregory P; Lipton, Stuart A

2015-01-01

Activation of the Kelch-like ECH-associated protein 1/nuclear factor (erythroid-derived 2)-like 2 and heat-shock protein 90/heat-shock factor-1 signal-transduction pathways plays a central role in combatting cellular oxidative damage and related endoplasmic reticulum stress. Electrophilic compounds have been shown to be activators of these transcription-mediated responses through S-alkylation of specific regulatory proteins. Previously, we reported that a prototype compound (D1, a small molecule representing a proelectrophilic, para-hydroquinone species) exhibited neuroprotective action by activating both of these pathways. We hypothesized that the para-hydroquinone moiety was critical for this activation because it enhanced transcription of these neuroprotective pathways to a greater degree than that of the corresponding ortho-hydroquinone isomer. This notion was based on the differential oxidation potentials of the isomers for the transformation of the hydroquinone to the active, electrophilic quinone species. Here, to further test this hypothesis, we synthesized a pair of para- and ortho-hydroquinone-based proelectrophilic compounds and measured their redox potentials using analytical cyclic voltammetry. The redox potential was then compared with functional biological activity, and the para-hydroquinones demonstrated a superior neuroprotective profile. © The Author(s) 2015.

Pathview Web: user friendly pathway visualization and data integration.

PubMed

Luo, Weijun; Pant, Gaurav; Bhavnasi, Yeshvant K; Blanchard, Steven G; Brouwer, Cory

2017-07-03

Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Discovery of potent and novel smoothened antagonists via structure-based virtual screening and biological assays.

PubMed

Lu, Wenfeng; Zhang, Dihua; Ma, Haikuo; Tian, Sheng; Zheng, Jiyue; Wang, Qin; Luo, Lusong; Zhang, Xiaohu

2018-05-23

The Hedgehog (Hh) signaling pathway plays a critical role in controlling patterning, growth and cell migration during embryonic development. Aberrant activation of Hh signaling has been linked to tumorigenesis in various cancers, such as basal cell carcinoma (BCC) and medulloblastoma. As a key member of the Hh pathway, the Smoothened (Smo) receptor, a member of the G protein-coupled receptor (GPCR) family, has emerged as an attractive therapeutic target for the treatment and prevention of human cancers. The recent determination of several crystal structures of Smo in complex with different antagonists offers the possibility to perform structure-based virtual screening for discovering potent Smo antagonists with distinct chemical scaffolds. In this study, based on the two Smo crystal complexes with the best capacity to distinguish the known Smo antagonists from decoys, the molecular docking-based virtual screening was conducted to identify promising Smo antagonists from ChemDiv library. A total of 21 structurally novel and diverse compounds were selected for experimental testing, and six of them exhibited significant inhibitory activity against the Hh pathway activation (IC 50  < 10 μM) in a GRE (Gli-responsive element) reporter gene assay. Specifically, the most potent compound (compound 20: 47 nM) showed comparable Hh signaling inhibition to vismodegib (46 nM). Compound 20 was further confirmed to be a potent Smo antagonist in a fluorescence based competitive binding assay. Optimization using substructure searching method led to the discovery of 12 analogues of compound 20 with decent Hh pathway inhibition activity, including four compounds with IC 50 lower than 1 μM. The important residues uncovered by binding free energy calculation (MM/GBSA) and binding free energy decomposition were highlighted and discussed. These findings suggest that the novel scaffold afforded by compound 20 can be used as a good starting point for further modification/optimization and the clarified interaction patterns may also guide us to find more potent Smo antagonists. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Discovery of novel hedgehog antagonists from cell-based screening: Isosteric modification of p38 bisamides as potent inhibitors of SMO.

PubMed

Yang, Bin; Hird, Alexander W; Russell, Daniel John; Fauber, Benjamin P; Dakin, Les A; Zheng, Xiaolan; Su, Qibin; Godin, Robert; Brassil, Patrick; Devereaux, Erik; Janetka, James W

2012-07-15

Cell-based subset screening of compounds using a Gli transcription factor reporter cell assay and shh stimulated cell differentiation assay identified a series of bisamide compounds as hedgehog pathway inhibitors with good potency. Using a ligand-based optimization strategy, heteroaryl groups were utilized as conformationally restricted amide isosteres replacing one of the amides which significantly increased their potency against SMO and the hedgehog pathway while decreasing activity against p38α kinase. We report herein the identification of advanced lead compounds such as imidazole 11c and 11f encompassing good p38α selectivity, low nanomolar potency in both cell assays, excellent physiochemical properties and in vivo pharmacokinetics. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Graph Based Interface for Representing Volume Visualization Results

NASA Technical Reports Server (NTRS)

Patten, James M.; Ma, Kwan-Liu

1998-01-01

This paper discusses a graph based user interface for representing the results of the volume visualization process. As images are rendered, they are connected to other images in a graph based on their rendering parameters. The user can take advantage of the information in this graph to understand how certain rendering parameter changes affect a dataset, making the visualization process more efficient. Because the graph contains more information than is contained in an unstructured history of images, the image graph is also helpful for collaborative visualization and animation.

Analyzing locomotion synthesis with feature-based motion graphs.

PubMed

Mahmudi, Mentar; Kallmann, Marcelo

2013-05-01

We propose feature-based motion graphs for realistic locomotion synthesis among obstacles. Among several advantages, feature-based motion graphs achieve improved results in search queries, eliminate the need of postprocessing for foot skating removal, and reduce the computational requirements in comparison to traditional motion graphs. Our contributions are threefold. First, we show that choosing transitions based on relevant features significantly reduces graph construction time and leads to improved search performances. Second, we employ a fast channel search method that confines the motion graph search to a free channel with guaranteed clearance among obstacles, achieving faster and improved results that avoid expensive collision checking. Lastly, we present a motion deformation model based on Inverse Kinematics applied over the transitions of a solution branch. Each transition is assigned a continuous deformation range that does not exceed the original transition cost threshold specified by the user for the graph construction. The obtained deformation improves the reachability of the feature-based motion graph and in turn also reduces the time spent during search. The results obtained by the proposed methods are evaluated and quantified, and they demonstrate significant improvements in comparison to traditional motion graph techniques.

A new graph-based method for pairwise global network alignment

PubMed Central

Klau, Gunnar W

2009-01-01

Background In addition to component-based comparative approaches, network alignments provide the means to study conserved network topology such as common pathways and more complex network motifs. Yet, unlike in classical sequence alignment, the comparison of networks becomes computationally more challenging, as most meaningful assumptions instantly lead to NP-hard problems. Most previous algorithmic work on network alignments is heuristic in nature. Results We introduce the graph-based maximum structural matching formulation for pairwise global network alignment. We relate the formulation to previous work and prove NP-hardness of the problem. Based on the new formulation we build upon recent results in computational structural biology and present a novel Lagrangian relaxation approach that, in combination with a branch-and-bound method, computes provably optimal network alignments. The Lagrangian algorithm alone is a powerful heuristic method, which produces solutions that are often near-optimal and – unlike those computed by pure heuristics – come with a quality guarantee. Conclusion Computational experiments on the alignment of protein-protein interaction networks and on the classification of metabolic subnetworks demonstrate that the new method is reasonably fast and has advantages over pure heuristics. Our software tool is freely available as part of the LISA library. PMID:19208162

Discovery of Boolean metabolic networks: integer linear programming based approach.

PubMed

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems

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

Sengupta, Dipanjan; Song, Shuaiwen; Agarwal, Kapil

2015-11-15

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host andmore » device.« less

Molecular graph convolutions: moving beyond fingerprints

NASA Astrophysics Data System (ADS)

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds

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

Kubic, William Louis; Jenkins, Rhodri W.; Moore, Cameron M.

Chemical pathways for converting biomass into fuels produce compounds for which key physical and chemical property data are unavailable. We developed an artificial neural network based group contribution method for estimating cetane and octane numbers that captures the complex dependence of fuel properties of pure compounds on chemical structure and is statistically superior to current methods.

Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds

DOE PAGES

Kubic, William Louis; Jenkins, Rhodri W.; Moore, Cameron M.; ...

2017-09-28

Chemical pathways for converting biomass into fuels produce compounds for which key physical and chemical property data are unavailable. We developed an artificial neural network based group contribution method for estimating cetane and octane numbers that captures the complex dependence of fuel properties of pure compounds on chemical structure and is statistically superior to current methods.

Analysis of Return and Forward Links from STARS' Flight Demonstration 1

NASA Technical Reports Server (NTRS)

Gering, James A.

2003-01-01

Space-based Telemetry And Range Safety (STARS) is a Kennedy Space Center (KSC) led proof-of-concept demonstration, which utilizes NASA's space network of Tracking and Data Relay Satellites (TDRS) as a pathway for launch and mission related information streams. Flight Demonstration 1 concluded on July 15,2003 with the seventh flight of a Low Power Transmitter (LPT) a Command and Data Handler (C&DH), a twelve channel GPS receiver and associated power supplies and amplifiers. The equipment flew on NASA's F-I5 aircraft at the Dryden Flight Research Center located at Edwards Air Force Base in California. During this NASA-ASEE Faculty Fellowship, the author participated in the collection and analysis of data from the seven flights comprising Flight Demonstration 1. Specifically, the author examined the forward and return links bit energy E(sub B) (in Watt-seconds) divided by the ambient radio frequency noise N(sub 0) (in Watts / Hertz). E(sub b)/N(sub 0) is commonly thought of as a signal-to-noise parameter, which characterizes a particular received radio frequency (RF) link. Outputs from the data analysis include the construction of time lines for all flights, production of graphs of range safety values for all seven flights, histograms of range safety E(sub b)/N(sub 0) values in five dB increments, calculation of associated averages and standard deviations, production of graphs of range user E(sub b)/N(sub 0) values for the all flights, production of graphs of AGC's and E(sub b)/N(sub 0) estimates for flight 1, recorded onboard, transmitted directly to the launch head and transmitted through TDRS. The data and graphs are being used to draw conclusions related to a lower than expected signal strength seen in the range safety return link.

Image understanding systems based on the unifying representation of perceptual and conceptual information and the solution of mid-level and high-level vision problems

NASA Astrophysics Data System (ADS)

Kuvychko, Igor

2001-10-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, that is an interpretation of visual information in terms of such knowledge models. A computer vision system based on such principles requires unifying representation of perceptual and conceptual information. Computer simulation models are built on the basis of graphs/networks. The ability of human brain to emulate similar graph/networks models is found. That means a very important shift of paradigm in our knowledge about brain from neural networks to the cortical software. Starting from the primary visual areas, brain analyzes an image as a graph-type spatial structure. Primary areas provide active fusion of image features on a spatial grid-like structure, where nodes are cortical columns. The spatial combination of different neighbor features cannot be described as a statistical/integral characteristic of the analyzed region, but uniquely characterizes such region itself. Spatial logic and topology naturally present in such structures. Mid-level vision processes like clustering, perceptual grouping, multilevel hierarchical compression, separation of figure from ground, etc. are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena like shape from shading, occlusion, etc. are results of such analysis. Such approach gives opportunity not only to explain frequently unexplainable results of the cognitive science, but also to create intelligent computer vision systems that simulate perceptional processes in both what and where visual pathways. Such systems can open new horizons for robotic and computer vision industries.

Kinase Pathway Database: An Integrated Protein-Kinase and NLP-Based Protein-Interaction Resource

PubMed Central

Koike, Asako; Kobayashi, Yoshiyuki; Takagi, Toshihisa

2003-01-01

Protein kinases play a crucial role in the regulation of cellular functions. Various kinds of information about these molecules are important for understanding signaling pathways and organism characteristics. We have developed the Kinase Pathway Database, an integrated database involving major completely sequenced eukaryotes. It contains the classification of protein kinases and their functional conservation, ortholog tables among species, protein–protein, protein–gene, and protein–compound interaction data, domain information, and structural information. It also provides an automatic pathway graphic image interface. The protein, gene, and compound interactions are automatically extracted from abstracts for all genes and proteins by natural-language processing (NLP).The method of automatic extraction uses phrase patterns and the GENA protein, gene, and compound name dictionary, which was developed by our group. With this database, pathways are easily compared among species using data with more than 47,000 protein interactions and protein kinase ortholog tables. The database is available for querying and browsing at http://kinasedb.ontology.ims.u-tokyo.ac.jp/. PMID:12799355

Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery.

PubMed

Drawnel, Faye Marie; Zhang, Jitao David; Küng, Erich; Aoyama, Natsuyo; Benmansour, Fethallah; Araujo Del Rosario, Andrea; Jensen Zoffmann, Sannah; Delobel, Frédéric; Prummer, Michael; Weibel, Franziska; Carlson, Coby; Anson, Blake; Iacone, Roberto; Certa, Ulrich; Singer, Thomas; Ebeling, Martin; Prunotto, Marco

2017-05-18

Today, novel therapeutics are identified in an environment which is intrinsically different from the clinical context in which they are ultimately evaluated. Using molecular phenotyping and an in vitro model of diabetic cardiomyopathy, we show that by quantifying pathway reporter gene expression, molecular phenotyping can cluster compounds based on pathway profiles and dissect associations between pathway activities and disease phenotypes simultaneously. Molecular phenotyping was applicable to compounds with a range of binding specificities and triaged false positives derived from high-content screening assays. The technique identified a class of calcium-signaling modulators that can reverse disease-regulated pathways and phenotypes, which was validated by structurally distinct compounds of relevant classes. Our results advocate for application of molecular phenotyping in early drug discovery, promoting biological relevance as a key selection criterion early in the drug development cascade. Copyright © 2017 Elsevier Ltd. All rights reserved.

A system view and analysis of essential hypertension.

PubMed

Botzer, Alon; Grossman, Ehud; Moult, John; Unger, Ron

2018-05-01

The goal of this study was to investigate genes associated with essential hypertension from a system perspective, making use of bioinformatic tools to gain insights that are not evident when focusing at a detail-based resolution. Using various databases (pathways, Genome Wide Association Studies, knockouts etc.), we compiled a set of about 200 genes that play a major role in hypertension and identified the interactions between them. This enabled us to create a protein-protein interaction network graph, from which we identified key elements, based on graph centrality analysis. Enriched gene regulatory elements (transcription factors and microRNAs) were extracted by motif finding techniques and knowledge-based tools. We found that the network is composed of modules associated with functions such as water retention, endothelial vasoconstriction, sympathetic activity and others. We identified the transcription factor SP1 and the two microRNAs miR27 (a and b) and miR548c-3p that seem to play a major role in regulating the network as they exert their control over several modules and are not restricted to specific functions. We also noticed that genes involved in metabolic diseases (e.g. insulin) are central to the network. We view the blood-pressure regulation mechanism as a system-of-systems, composed of several contributing subsystems and pathways rather than a single module. The system is regulated by distributed elements. Understanding this mode of action can lead to a more precise treatment and drug target discovery. Our analysis suggests that insulin plays a primary role in hypertension, highlighting the tight link between essential hypertension and diseases associated with the metabolic syndrome.

Clique-based data mining for related genes in a biomedical database.

PubMed

Matsunaga, Tsutomu; Yonemori, Chikara; Tomita, Etsuji; Muramatsu, Masaaki

2009-07-01

Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph. We constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the Online Mendelian Inheritance in Man (OMIM) database. We obtained over 20,000 sets of related genes (called 'gene modules') by enumerating cliques computationally. The modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. The results of experiments using 'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes. We presented a data mining approach extracting related genes by enumerating cliques. The extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.

Text categorization of biomedical data sets using graph kernels and a controlled vocabulary.

PubMed

Bleik, Said; Mishra, Meenakshi; Huan, Jun; Song, Min

2013-01-01

Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.

Optimization of memory use of fragment extension-based protein-ligand docking with an original fast minimum cost flow algorithm.

PubMed

Yanagisawa, Keisuke; Komine, Shunta; Kubota, Rikuto; Ohue, Masahito; Akiyama, Yutaka

2018-06-01

The need to accelerate large-scale protein-ligand docking in virtual screening against a huge compound database led researchers to propose a strategy that entails memorizing the evaluation result of the partial structure of a compound and reusing it to evaluate other compounds. However, the previous method required frequent disk accesses, resulting in insufficient acceleration. Thus, more efficient memory usage can be expected to lead to further acceleration, and optimal memory usage could be achieved by solving the minimum cost flow problem. In this research, we propose a fast algorithm for the minimum cost flow problem utilizing the characteristics of the graph generated for this problem as constraints. The proposed algorithm, which optimized memory usage, was approximately seven times faster compared to existing minimum cost flow algorithms. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

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

Sengupta, Dipanjan; Agarwal, Kapil; Song, Shuaiwen

2015-09-30

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the hostmore » and the device.« less

Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method.

PubMed

Gao, Hong; Jin, Hui; Li, Guijun

2018-01-01

This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election. The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3. Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant. We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.

Molecular graph convolutions: moving beyond fingerprints

PubMed Central

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-01-01

Molecular “fingerprints” encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement. PMID:27558503

Mathematical foundations of the GraphBLAS

DOE PAGES

Kepner, Jeremy; Aaltonen, Peter; Bader, David; ...

2016-12-01

The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based graph algorithms to the broadest possible audience. Mathematically, the GraphBLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This study provides an introduction to the mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, themore » two are easily connected by matrix multiplication. A key feature of matrix mathematics is that a very small number of matrix operations can be used to manipulate a very wide range of graphs. This composability of a small number of operations is the foundation of the GraphBLAS. A standard such as the GraphBLAS can only be effective if it has low performance overhead. Finally, performance measurements of prototype GraphBLAS implementations indicate that the overhead is low.« less

Structure Based Virtual Screening Studies to Identify Novel Potential Compounds for GPR142 and Their Relative Dynamic Analysis for Study of Type 2 Diabetes

NASA Astrophysics Data System (ADS)

Kaushik, Aman C.; Kumar, Sanjay; Wei, Dong Q.; Sahi, Shakti

2018-02-01

GPR142 (G protein receptor 142) is a novel orphan GPCR (G protein coupled receptor) belonging to ‘Class A’ of GPCR family and expressed in beta cells of pancreas. In this study, we reported the structure based virtual screening to identify the hit compounds which can be developed as leads for potential agonists. The results were validated through induced fit docking, pharmacophore modeling and system biology approaches. Since, there is no solved crystal structure of GPR142, we attempted to predict the 3D structure followed by validation and then identification of active site using threading and ab initio methods. Also, structure based virtual screening was performed against a total of 1171519 compounds from different libraries and only top 20 best hit compounds were screened and analyzed. Moreover, the biochemical pathway of GPR142 complex with screened compound2 was also designed and compared with experimental data. Interestingly, compound2 showed an increase in insulin production via Gq mediated signaling pathway suggesting the possible role of novel GPR142 agonists in therapy against type 2 diabetes.

New methods for analyzing semantic graph based assessments in science education

NASA Astrophysics Data System (ADS)

Vikaros, Lance Steven

This research investigated how the scoring of semantic graphs (known by many as concept maps) could be improved and automated in order to address issues of inter-rater reliability and scalability. As part of the NSF funded SENSE-IT project to introduce secondary school science students to sensor networks (NSF Grant No. 0833440), semantic graphs illustrating how temperature change affects water ecology were collected from 221 students across 16 schools. The graphing task did not constrain students' use of terms, as is often done with semantic graph based assessment due to coding and scoring concerns. The graphing software used provided real-time feedback to help students learn how to construct graphs, stay on topic and effectively communicate ideas. The collected graphs were scored by human raters using assessment methods expected to boost reliability, which included adaptations of traditional holistic and propositional scoring methods, use of expert raters, topical rubrics, and criterion graphs. High levels of inter-rater reliability were achieved, demonstrating that vocabulary constraints may not be necessary after all. To investigate a new approach to automating the scoring of graphs, thirty-two different graph features characterizing graphs' structure, semantics, configuration and process of construction were then used to predict human raters' scoring of graphs in order to identify feature patterns correlated to raters' evaluations of graphs' topical accuracy and complexity. Results led to the development of a regression model able to predict raters' scoring with 77% accuracy, with 46% accuracy expected when used to score new sets of graphs, as estimated via cross-validation tests. Although such performance is comparable to other graph and essay based scoring systems, cross-context testing of the model and methods used to develop it would be needed before it could be recommended for widespread use. Still, the findings suggest techniques for improving the reliability and scalability of semantic graph based assessments without requiring constraint of how ideas are expressed.

On Functional Module Detection in Metabolic Networks

PubMed Central

Koch, Ina; Ackermann, Jörg

2013-01-01

Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145

PathJam: a new service for integrating biological pathway information.

PubMed

Glez-Peña, Daniel; Reboiro-Jato, Miguel; Domínguez, Rubén; Gómez-López, Gonzalo; Pisano, David G; Fdez-Riverola, Florentino

2010-10-28

Biological pathways are crucial to much of the scientific research today including the study of specific biological processes related with human diseases. PathJam is a new comprehensive and freely accessible web-server application integrating scattered human pathway annotation from several public sources. The tool has been designed for both (i) being intuitive for wet-lab users providing statistical enrichment analysis of pathway annotations and (ii) giving support to the development of new integrative pathway applications. PathJam’s unique features and advantages include interactive graphs linking pathways and genes of interest, downloadable results in fully compatible formats, GSEA compatible output files and a standardized RESTful API.

Graph Kernels for Molecular Similarity.

PubMed

Rupp, Matthias; Schneider, Gisbert

2010-04-12

Molecular similarity measures are important for many cheminformatics applications like ligand-based virtual screening and quantitative structure-property relationships. Graph kernels are formal similarity measures defined directly on graphs, such as the (annotated) molecular structure graph. Graph kernels are positive semi-definite functions, i.e., they correspond to inner products. This property makes them suitable for use with kernel-based machine learning algorithms such as support vector machines and Gaussian processes. We review the major types of kernels between graphs (based on random walks, subgraphs, and optimal assignments, respectively), and discuss their advantages, limitations, and successful applications in cheminformatics. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Xtalk: a path-based approach for identifying crosstalk between signaling pathways

PubMed Central

Tegge, Allison N.; Sharp, Nicholas; Murali, T. M.

2016-01-01

Motivation: Cells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk. Existing computational methods to discover such pathway pairs rely on simple overlap statistics. Results: We present Xtalk, a path-based approach for identifying pairs of pathways that may crosstalk. Xtalk computes the statistical significance of the average length of multiple short paths that connect receptors in one pathway to the transcription factors in another. By design, Xtalk reports the precise interactions and mechanisms that support the identified crosstalk. We applied Xtalk to signaling pathways in the KEGG and NCI-PID databases. We manually curated a gold standard set of 132 crosstalking pathway pairs and a set of 140 pairs that did not crosstalk, for which Xtalk achieved an area under the receiver operator characteristic curve of 0.65, a 12% improvement over the closest competing approach. The area under the receiver operator characteristic curve varied with the pathway, suggesting that crosstalk should be evaluated on a pathway-by-pathway level. We also analyzed an extended set of 658 pathway pairs in KEGG and to a set of more than 7000 pathway pairs in NCI-PID. For the top-ranking pairs, we found substantial support in the literature (81% for KEGG and 78% for NCI-PID). We provide examples of networks computed by Xtalk that accurately recovered known mechanisms of crosstalk. Availability and implementation: The XTALK software is available at http://bioinformatics.cs.vt.edu/~murali/software. Crosstalk networks are available at http://graphspace.org/graphs?tags=2015-bioinformatics-xtalk. Contact: ategge@vt.edu, murali@cs.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26400040

Improved Release and Metabolism of Flavonoids by Steered Fermentation Processes: A Review

PubMed Central

Nguyen Thai, Huynh; Van Camp, John; Smagghe, Guy; Raes, Katleen

2014-01-01

This paper provides an overview on steered fermentation processes to release phenolic compounds from plant-based matrices, as well as on their potential application to convert phenolic compounds into unique metabolites. The ability of fermentation to improve the yield and to change the profile of phenolic compounds is mainly due to the release of bound phenolic compounds, as a consequence of the degradation of the cell wall structure by microbial enzymes produced during fermentation. Moreover, the microbial metabolism of phenolic compounds results in a large array of new metabolites through different bioconversion pathways such as glycosylation, deglycosylation, ring cleavage, methylation, glucuronidation and sulfate conjugation, depending on the microbial strains and substrates used. A whole range of metabolites is produced, however metabolic pathways related to the formation and bioactivities, and often quantification of the metabolites are highly underinvestigated. This strategy could have potential to produce extracts with a high-added value from plant-based matrices. PMID:25347275

Magnetic and magnetocaloric properties of HoCr0.75Fe0.25O3 compound

NASA Astrophysics Data System (ADS)

Kotnana, Ganesh; Babu, P. D.; Jammalamadaka, S. Narayana

2018-05-01

We report on the magnetic and magnetocaloric properties of HoCr0.75Fe0.25O3 compound around the Néel temperature (TN), which is due to Cr3+ ordering. Susceptibility (χ) vs. temperature (T) graph of HoCr0.75Fe0.25O3 compound infer two transitions due to the ordering of Cr3+ moments (TN ˜ 155 K) and Ho3+ moments (TNHo ˜ 8 K). Magnetic entropy (-ΔSM) value of 1.14 J kg-1 K-1 around 157.5 K with a magnetic field (H) of 90 kOe is attributed to antiferromagnetic (AFM) ordering of Cr3+ moments. A maximum value of adiabatic temperature (ΔTad) ˜ 0.41 K around TN is obtained and is found to increases with applied magnetic field. Negative slope for H/M vs. M2 graph is evident for HoCr0.75Fe0.25O3 compound below TN, which indicates the first order phase transition. Quantified values of -ΔSM and ΔTad open the way to explore rare earth orthochromites for the MCE properties and refrigeration applications.

Indexing molecules with chemical graph identifiers.

PubMed

Gregori-Puigjané, Elisabet; Garriga-Sust, Rut; Mestres, Jordi

2011-09-01

Fast and robust algorithms for indexing molecules have been historically considered strategic tools for the management and storage of large chemical libraries. This work introduces a modified and further extended version of the molecular equivalence number naming adaptation of the Morgan algorithm (J Chem Inf Comput Sci 2001, 41, 181-185) for the generation of a chemical graph identifier (CGI). This new version corrects for the collisions recognized in the original adaptation and includes the ability to deal with graph canonicalization, ensembles (salts), and isomerism (tautomerism, regioisomerism, optical isomerism, and geometrical isomerism) in a flexible manner. Validation of the current CGI implementation was performed on the open NCI database and the drug-like subset of the ZINC database containing 260,071 and 5,348,089 structures, respectively. The results were compared with those obtained with some of the most widely used indexing codes, such as the CACTVS hash code and the new InChIKey. The analyses emphasize the fact that compound management activities, like duplicate analysis of chemical libraries, are sensitive to the exact definition of compound uniqueness and thus still depend, to a minor extent, on the type and flexibility of the molecular index being used. Copyright © 2011 Wiley Periodicals, Inc.

GrouseFlocks: steerable exploration of graph hierarchy space.

PubMed

Archambault, Daniel; Munzner, Tamara; Auber, David

2008-01-01

Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domain-specific attributes associated with the nodes and edges, which could be used to create many possible hierarchies providing unique views of the input graph. GrouseFlocks is a system for the exploration of this graph hierarchy space. By allowing users to see several different possible hierarchies on the same graph, the system helps users investigate graph hierarchy space instead of a single fixed hierarchy. GrouseFlocks provides a simple set of operations so that users can create and modify their graph hierarchies based on selections. These selections can be made manually or based on patterns in the attribute data provided with the graph. It provides feedback to the user within seconds, allowing interactive exploration of this space.

Discriminating Drug-Like Compounds by Partition Trees with Quantum Similarity Indices and Graph Invariants.

PubMed

Julián-Ortiz, Jesus V de; Gozalbes, Rafael; Besalú, Emili

2016-01-01

The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

PubMed

Ma, Hong-Wu; Zhao, Xue-Ming; Yuan, Ying-Jin; Zeng, An-Ping

2004-08-12

Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. http://genome.gbf.de/bioinformatics/

Measuring the hierarchy of feedforward networks

NASA Astrophysics Data System (ADS)

Corominas-Murtra, Bernat; Rodríguez-Caso, Carlos; Goñi, Joaquín; Solé, Ricard

2011-03-01

In this paper we explore the concept of hierarchy as a quantifiable descriptor of ordered structures, departing from the definition of three conditions to be satisfied for a hierarchical structure: order, predictability, and pyramidal structure. According to these principles, we define a hierarchical index taking concepts from graph and information theory. This estimator allows to quantify the hierarchical character of any system susceptible to be abstracted in a feedforward causal graph, i.e., a directed acyclic graph defined in a single connected structure. Our hierarchical index is a balance between this predictability and pyramidal condition by the definition of two entropies: one attending the onward flow and the other for the backward reversion. We show how this index allows to identify hierarchical, antihierarchical, and nonhierarchical structures. Our formalism reveals that departing from the defined conditions for a hierarchical structure, feedforward trees and the inverted tree graphs emerge as the only causal structures of maximal hierarchical and antihierarchical systems respectively. Conversely, null values of the hierarchical index are attributed to a number of different configuration networks; from linear chains, due to their lack of pyramid structure, to full-connected feedforward graphs where the diversity of onward pathways is canceled by the uncertainty (lack of predictability) when going backward. Some illustrative examples are provided for the distinction among these three types of hierarchical causal graphs.

Identification of small molecule compounds that inhibit the HIF-1 signaling pathway

PubMed Central

2009-01-01

Background Hypoxia-inducible factor-1 (HIF-1) is the major hypoxia-regulated transcription factor that regulates cellular responses to low oxygen environments. HIF-1 is composed of two subunits: hypoxia-inducible HIF-1α and constitutively-expressed HIF-1β. During hypoxic conditions, HIF-1α heterodimerizes with HIF-1β and translocates to the nucleus where the HIF-1 complex binds to the hypoxia-response element (HRE) and activates expression of target genes implicated in cell growth and survival. HIF-1α protein expression is elevated in many solid tumors, including those of the cervix and brain, where cells that are the greatest distance from blood vessels, and therefore the most hypoxic, express the highest levels of HIF-1α. Therapeutic blockade of the HIF-1 signaling pathway in cancer cells therefore provides an attractive strategy for development of anticancer drugs. To identify small molecule inhibitors of the HIF-1 pathway, we have developed a cell-based reporter gene assay and screened a large compound library by using a quantitative high-throughput screening (qHTS) approach. Results The assay is based upon a β-lactamase reporter under the control of a HRE. We have screened approximate 73,000 compounds by qHTS, with each compound tested over a range of seven to fifteen concentrations. After qHTS we have rapidly identified three novel structural series of HIF-1 pathway Inhibitors. Selected compounds in these series were also confirmed as inhibitors in a HRE β-lactamase reporter gene assay induced by low oxygen and in a VEGF secretion assay. Three of the four selected compounds tested showed significant inhibition of hypoxia-induced HIF-1α accumulation by western blot analysis. Conclusion The use of β-lactamase reporter gene assays, in combination with qHTS, enabled the rapid identification and prioritization of inhibitors specific to the hypoxia induced signaling pathway. PMID:20003191

Classification of Chemical Compounds to Support Complex Queries in a Pathway Database

PubMed Central

Weidemann, Andreas; Kania, Renate; Peiss, Christian; Rojas, Isabel

2004-01-01

Data quality in biological databases has become a topic of great discussion. To provide high quality data and to deal with the vast amount of biochemical data, annotators and curators need to be supported by software that carries out part of their work in an (semi-) automatic manner. The detection of errors and inconsistencies is a part that requires the knowledge of domain experts, thus in most cases it is done manually, making it very expensive and time-consuming. This paper presents two tools to partially support the curation of data on biochemical pathways. The tool enables the automatic classification of chemical compounds based on their respective SMILES strings. Such classification allows the querying and visualization of biochemical reactions at different levels of abstraction, according to the level of detail at which the reaction participants are described. Chemical compounds can be classified in a flexible manner based on different criteria. The support of the process of data curation is provided by facilitating the detection of compounds that are identified as different but that are actually the same. This is also used to identify similar reactions and, in turn, pathways. PMID:18629066

Validation of RetroPath, a computer-aided design tool for metabolic pathway engineering.

PubMed

Fehér, Tamás; Planson, Anne-Gaëlle; Carbonell, Pablo; Fernández-Castané, Alfred; Grigoras, Ioana; Dariy, Ekaterina; Perret, Alain; Faulon, Jean-Loup

2014-11-01

Metabolic engineering has succeeded in biosynthesis of numerous commodity or high value compounds. However, the choice of pathways and enzymes used for production was many times made ad hoc, or required expert knowledge of the specific biochemical reactions. In order to rationalize the process of engineering producer strains, we developed the computer-aided design (CAD) tool RetroPath that explores and enumerates metabolic pathways connecting the endogenous metabolites of a chassis cell to the target compound. To experimentally validate our tool, we constructed 12 top-ranked enzyme combinations producing the flavonoid pinocembrin, four of which displayed significant yields. Namely, our tool queried the enzymes found in metabolic databases based on their annotated and predicted activities. Next, it ranked pathways based on the predicted efficiency of the available enzymes, the toxicity of the intermediate metabolites and the calculated maximum product flux. To implement the top-ranking pathway, our procedure narrowed down a list of nine million possible enzyme combinations to 12, a number easily assembled and tested. One round of metabolic network optimization based on RetroPath output further increased pinocembrin titers 17-fold. In total, 12 out of the 13 enzymes tested in this work displayed a relative performance that was in accordance with its predicted score. These results validate the ranking function of our CAD tool, and open the way to its utilization in the biosynthesis of novel compounds. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Supporting Fourth Graders' Ability to Interpret Graphs through Real-Time Graphing Technology: A Preliminary Study

ERIC Educational Resources Information Center

Deniz, Hasan; Dulger, Mehmet F.

2012-01-01

This study examined to what extent inquiry-based instruction supported with real-time graphing technology improves fourth grader's ability to interpret graphs as representations of physical science concepts such as motion and temperature. This study also examined whether there is any difference between inquiry-based instruction supported with…

Generalized graph states based on Hadamard matrices

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

Cui, Shawn X.; Yu, Nengkun; Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario N1G 2W1

2015-07-15

Graph states are widely used in quantum information theory, including entanglement theory, quantum error correction, and one-way quantum computing. Graph states have a nice structure related to a certain graph, which is given by either a stabilizer group or an encoding circuit, both can be directly given by the graph. To generalize graph states, whose stabilizer groups are abelian subgroups of the Pauli group, one approach taken is to study non-abelian stabilizers. In this work, we propose to generalize graph states based on the encoding circuit, which is completely determined by the graph and a Hadamard matrix. We study themore » entanglement structures of these generalized graph states and show that they are all maximally mixed locally. We also explore the relationship between the equivalence of Hadamard matrices and local equivalence of the corresponding generalized graph states. This leads to a natural generalization of the Pauli (X, Z) pairs, which characterizes the local symmetries of these generalized graph states. Our approach is also naturally generalized to construct graph quantum codes which are beyond stabilizer codes.« less

A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data.

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

Mayo, Jackson R.; Kegelmeyer, W. Philip, Jr.; Wong, Matthew H.

The monitoring and system analysis of high performance computing (HPC) clusters is of increasing importance to the HPC community. Analysis of HPC job data can be used to characterize system usage and diagnose and examine failure modes and their effects. This analysis is not straightforward, however, due to the complex relationships that exist between jobs. These relationships are based on a number of factors, including shared compute nodes between jobs, proximity of jobs in time, etc. Graph-based techniques represent an approach that is particularly well suited to this problem, and provide an effective technique for discovering important relationships in jobmore » queuing and execution data. The efficacy of these techniques is rooted in the use of a semantic graph as a knowledge representation tool. In a semantic graph job data, represented in a combination of numerical and textual forms, can be flexibly processed into edges, with corresponding weights, expressing relationships between jobs, nodes, users, and other relevant entities. This graph-based representation permits formal manipulation by a number of analysis algorithms. This report presents a methodology and software implementation that leverages semantic graph-based techniques for the system-level monitoring and analysis of HPC clusters based on job queuing and execution data. Ontology development and graph synthesis is discussed with respect to the domain of HPC job data. The framework developed automates the synthesis of graphs from a database of job information. It also provides a front end, enabling visualization of the synthesized graphs. Additionally, an analysis engine is incorporated that provides performance analysis, graph-based clustering, and failure prediction capabilities for HPC systems.« less

A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.

PubMed

Wei, Wei; Gao, Bin; Liu, Tie-Yan; Wang, Taifeng; Li, Guohui; Li, Hang

2016-04-01

Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.

N,N-Dimethyl-N′-[3-(trifluoro­methyl)­phenyl]­urea

PubMed Central

Yu, Da-sheng; Li, Fang-shi; Yao, Wei; Liu, Yin-hong; Lu, Chui

2008-01-01

The title compound, C10H11F3N2O, is an important urea-based herbicide. In the crystal structure, the mol­ecular packing is stabilized by two intra­molecular C—H⋯O hydrogen bonds and one inter­molecular N—H⋯O hydrogen bond, generating a C(4) graph-set motif running parallel to the [001] direction. The F atoms are disordered over two sites, with occupancies of 0.176 (9) and 0.824 (9). PMID:21202857

Coordinates and intervals in graph-based reference genomes.

PubMed

Rand, Knut D; Grytten, Ivar; Nederbragt, Alexander J; Storvik, Geir O; Glad, Ingrid K; Sandve, Geir K

2017-05-18

It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes. We formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures. We show the advantage of our methods by representing genes on a graph-based representation of the newest assembly of the human genome (GRCh38) and its alternative loci for regions that are highly variable. More complex reference genomes, containing alternative loci, require methods to represent genomic data on these structures. Our proposed notation for genomic intervals makes it possible to fully utilize the alternative loci of the GRCh38 assembly and potential future graph-based reference genomes. We have made a Python package for representing such intervals on offset-based coordinate systems, available at https://github.com/uio-cels/offsetbasedgraph . An interactive web-tool using this Python package to visualize genes on a graph created from GRCh38 is available at https://github.com/uio-cels/genomicgraphcoords .

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

PubMed

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

Seeing How Money Grows.

ERIC Educational Resources Information Center

Metz, James

2001-01-01

Describes an activity designed to help students connect the ideas of linear growth and exponential growth through graphs of the future value of accounts that earn simple interest and accounts that earn compound interest. Includes worksheets and solutions. (KHR)

Computer-assisted Crystallization.

ERIC Educational Resources Information Center

Semeister, Joseph J., Jr.; Dowden, Edward

1989-01-01

To avoid a tedious task for recording temperature, a computer was used for calculating the heat of crystallization for the compound sodium thiosulfate. Described are the computer-interfacing procedures. Provides pictures of laboratory equipment and typical graphs from experiments. (YP)

A comparison of video modeling, text-based instruction, and no instruction for creating multiple baseline graphs in Microsoft Excel.

PubMed

Tyner, Bryan C; Fienup, Daniel M

2015-09-01

Graphing is socially significant for behavior analysts; however, graphing can be difficult to learn. Video modeling (VM) may be a useful instructional method but lacks evidence for effective teaching of computer skills. A between-groups design compared the effects of VM, text-based instruction, and no instruction on graphing performance. Participants who used VM constructed graphs significantly faster and with fewer errors than those who used text-based instruction or no instruction. Implications for instruction are discussed. © Society for the Experimental Analysis of Behavior.

Graph reconstruction using covariance-based methods.

PubMed

Sulaimanov, Nurgazy; Koeppl, Heinz

2016-12-01

Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data.

Constructing compact and effective graphs for recommender systems via node and edge aggregations

DOE PAGES

Lee, Sangkeun; Kahng, Minsuk; Lee, Sang-goo

2014-12-10

Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naive graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na ve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we proposemore » node and edge aggregation approaches to constructing compact and e ective graphs called Factor-Item bipartite graphs by aggregating nodes and edges of a log-metadata graph. Furthermore, experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality.« less

graphkernels: R and Python packages for graph comparison

PubMed Central

Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-01-01

Abstract Summary Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. Availability and implementation The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. Contact mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch Supplementary information Supplementary data are available online at Bioinformatics. PMID:29028902

graphkernels: R and Python packages for graph comparison.

PubMed

Sugiyama, Mahito; Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-02-01

Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch. Supplementary data are available online at Bioinformatics. © The Author(s) 2017. Published by Oxford University Press.

Noncontiguous atom matching structural similarity function.

PubMed

Teixeira, Ana L; Falcao, Andre O

2013-10-28

Measuring similarity between molecules is a fundamental problem in cheminformatics. Given that similar molecules tend to have similar physical, chemical, and biological properties, the notion of molecular similarity plays an important role in the exploration of molecular data sets, query-retrieval in molecular databases, and in structure-property/activity modeling. Various methods to define structural similarity between molecules are available in the literature, but so far none has been used with consistent and reliable results for all situations. We propose a new similarity method based on atom alignment for the analysis of structural similarity between molecules. This method is based on the comparison of the bonding profiles of atoms on comparable molecules, including features that are seldom found in other structural or graph matching approaches like chirality or double bond stereoisomerism. The similarity measure is then defined on the annotated molecular graph, based on an iterative directed graph similarity procedure and optimal atom alignment between atoms using a pairwise matching algorithm. With the proposed approach the similarities detected are more intuitively understood because similar atoms in the molecules are explicitly shown. This noncontiguous atom matching structural similarity method (NAMS) was tested and compared with one of the most widely used similarity methods (fingerprint-based similarity) using three difficult data sets with different characteristics. Despite having a higher computational cost, the method performed well being able to distinguish either different or very similar hydrocarbons that were indistinguishable using a fingerprint-based approach. NAMS also verified the similarity principle using a data set of structurally similar steroids with differences in the binding affinity to the corticosteroid binding globulin receptor by showing that pairs of steroids with a high degree of similarity (>80%) tend to have smaller differences in the absolute value of binding activity. Using a highly diverse set of compounds with information about the monoamine oxidase inhibition level, the method was also able to recover a significantly higher average fraction of active compounds when the seed is active for different cutoff threshold values of similarity. Particularly, for the cutoff threshold values of 86%, 93%, and 96.5%, NAMS was able to recover a fraction of actives of 0.57, 0.63, and 0.83, respectively, while the fingerprint-based approach was able to recover a fraction of actives of 0.41, 0.40, and 0.39, respectively. NAMS is made available freely for the whole community in a simple Web based tool as well as the Python source code at http://nams.lasige.di.fc.ul.pt/.

An MBO Scheme for Minimizing the Graph Ohta-Kawasaki Functional

NASA Astrophysics Data System (ADS)

van Gennip, Yves

2018-06-01

We study a graph-based version of the Ohta-Kawasaki functional, which was originally introduced in a continuum setting to model pattern formation in diblock copolymer melts and has been studied extensively as a paradigmatic example of a variational model for pattern formation. Graph-based problems inspired by partial differential equations (PDEs) and variational methods have been the subject of many recent papers in the mathematical literature, because of their applications in areas such as image processing and data classification. This paper extends the area of PDE inspired graph-based problems to pattern-forming models, while continuing in the tradition of recent papers in the field. We introduce a mass conserving Merriman-Bence-Osher (MBO) scheme for minimizing the graph Ohta-Kawasaki functional with a mass constraint. We present three main results: (1) the Lyapunov functionals associated with this MBO scheme Γ -converge to the Ohta-Kawasaki functional (which includes the standard graph-based MBO scheme and total variation as a special case); (2) there is a class of graphs on which the Ohta-Kawasaki MBO scheme corresponds to a standard MBO scheme on a transformed graph and for which generalized comparison principles hold; (3) this MBO scheme allows for the numerical computation of (approximate) minimizers of the graph Ohta-Kawasaki functional with a mass constraint.

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations.

PubMed

Zhao, Jian; Glueck, Michael; Breslav, Simon; Chevalier, Fanny; Khan, Azam

2017-01-01

User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

Automatic Molecular Design using Evolutionary Techniques

NASA Technical Reports Server (NTRS)

Globus, Al; Lawton, John; Wipke, Todd; Saini, Subhash (Technical Monitor)

1998-01-01

Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population of random molecules. The population is then evolved towards greater fitness by randomly combining parts of the better individuals to create new molecules. These new molecules then replace some of the worst molecules in the population. The unique aspect of our approach is that we apply genetic crossover to molecules represented by graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any possible molecule given an appropriate fitness function and a population containing both rings and chains. Prior work evolved strings or trees that were subsequently processed to generate molecular graphs. In principle, genetic graph software should be able to evolve other graph representable systems such as circuits, transportation networks, metabolic pathways, computer networks, etc.

Interleukins and their signaling pathways in the Reactome biological pathway database.

PubMed

Jupe, Steve; Ray, Keith; Roca, Corina Duenas; Varusai, Thawfeek; Shamovsky, Veronica; Stein, Lincoln; D'Eustachio, Peter; Hermjakob, Henning

2018-04-01

There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models. Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on a Web site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license. Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include as much molecular detail as possible and are linked to literature citations that contain supporting experimental details. All newly created events undergo a peer-review process before they are added to the database and made available on the associated Web site. New content is added quarterly. The 63rd release of Reactome in December 2017 contains 10,996 human proteins participating in 11,426 events in 2,179 pathways. In addition, analytic tools allow data set submission for the identification and visualization of pathway enrichment and representation of expression profiles as an overlay on Reactome pathways. Protein-protein and compound-protein interactions from several sources, including custom user data sets, can be added to extend pathways. Pathway diagrams and analytic result displays can be downloaded as editable images, human-readable reports, and files in several standard formats that are suitable for computational reuse. Reactome content is available programmatically through a REpresentational State Transfer (REST)-based content service and as a Neo4J graph database. Signaling pathways for IL-1 to IL-38 are hierarchically classified within the pathway "signaling by interleukins." The classification used is largely derived from Akdis et al. The addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. This information can be extended easily as new discoveries become accepted as the consensus in the field. A key aim for the future is to increase coverage of gene expression changes induced by interleukin signaling. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

AOP: An R Package For Sufficient Causal Analysis in Pathway ...

EPA Pesticide Factsheets

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharma-cology, toxicology and risk assessment. By identifying these sufficient causal key events, we have fewer events to monitor for a pathway, thereby decreasing assay costs and time, while maximizing the value of the information. I have developed the “aop” package which uses backdoor analysis of causal net-works to identify these minimal sets of key events that are suf-ficient for making causal predictions. Availability and Implementation: The source and binary are available online through the Bioconductor project (http://www.bioconductor.org/) as an R package titled “aop”. The R/Bioconductor package runs within the R statistical envi-ronment. The package has functions that can take pathways (as directed graphs) formatted as a Cytoscape JSON file as input, or pathways can be represented as directed graphs us-ing the R/Bioconductor “graph” package. The “aop” package has functions that can perform backdoor analysis to identify the minimal set of key events for making causal predictions.Contact: burgoon.lyle@epa.gov This paper describes an R/Bioconductor package that was developed to facilitate the identification of key events within an AOP that are the minimal set of sufficient key events that need to be tested/monit

An internet graph model based on trade-off optimization

NASA Astrophysics Data System (ADS)

Alvarez-Hamelin, J. I.; Schabanel, N.

2004-03-01

This paper presents a new model for the Internet graph (AS graph) based on the concept of heuristic trade-off optimization, introduced by Fabrikant, Koutsoupias and Papadimitriou in[CITE] to grow a random tree with a heavily tailed degree distribution. We propose here a generalization of this approach to generate a general graph, as a candidate for modeling the Internet. We present the results of our simulations and an analysis of the standard parameters measured in our model, compared with measurements from the physical Internet graph.

PRIMAL: Page Rank-Based Indoor Mapping and Localization Using Gene-Sequenced Unlabeled WLAN Received Signal Strength

PubMed Central

Zhou, Mu; Zhang, Qiao; Xu, Kunjie; Tian, Zengshan; Wang, Yanmeng; He, Wei

2015-01-01

Due to the wide deployment of wireless local area networks (WLAN), received signal strength (RSS)-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL) by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM). Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR) algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization. PMID:26404274

Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance.

PubMed

Brubaker, Douglas; Difeo, Analisa; Chen, Yanwen; Pearl, Taylor; Zhai, Kaide; Bebek, Gurkan; Chance, Mark; Barnholtz-Sloan, Jill

2014-01-01

The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.

Classification of ligand molecules in PDB with fast heuristic graph match algorithm COMPLIG.

PubMed

Saito, Mihoko; Takemura, Naomi; Shirai, Tsuyoshi

2012-12-14

A fast heuristic graph-matching algorithm, COMPLIG, was devised to classify the small-molecule ligands in the Protein Data Bank (PDB), which are currently not properly classified on structure basis. By concurrently classifying proteins and ligands, we determined the most appropriate parameter for categorizing ligands to be more than 60% identity of atoms and bonds between molecules, and we classified 11,585 types of ligands into 1946 clusters. Although the large clusters were composed of nucleotides or amino acids, a significant presence of drug compounds was also observed. Application of the system to classify the natural ligand status of human proteins in the current database suggested that, at most, 37% of the experimental structures of human proteins were in complex with natural ligands. However, protein homology- and/or ligand similarity-based modeling was implied to provide models of natural interactions for an additional 28% of the total, which might be used to increase the knowledge of intrinsic protein-metabolite interactions. Copyright © 2012 Elsevier Ltd. All rights reserved.

jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints

PubMed Central

2011-01-01

Background The decomposition of a chemical graph is a convenient approach to encode information of the corresponding organic compound. While several commercial toolkits exist to encode molecules as so-called fingerprints, only a few open source implementations are available. The aim of this work is to introduce a library for exactly defined molecular decompositions, with a strong focus on the application of these features in machine learning and data mining. It provides several options such as search depth, distance cut-offs, atom- and pharmacophore typing. Furthermore, it provides the functionality to combine, to compare, or to export the fingerprints into several formats. Results We provide a Java 1.6 library for the decomposition of chemical graphs based on the open source Chemistry Development Kit toolkit. We reimplemented popular fingerprinting algorithms such as depth-first search fingerprints, extended connectivity fingerprints, autocorrelation fingerprints (e.g. CATS2D), radial fingerprints (e.g. Molprint2D), geometrical Molprint, atom pairs, and pharmacophore fingerprints. We also implemented custom fingerprints such as the all-shortest path fingerprint that only includes the subset of shortest paths from the full set of paths of the depth-first search fingerprint. As an application of jCompoundMapper, we provide a command-line executable binary. We measured the conversion speed and number of features for each encoding and described the composition of the features in detail. The quality of the encodings was tested using the default parametrizations in combination with a support vector machine on the Sutherland QSAR data sets. Additionally, we benchmarked the fingerprint encodings on the large-scale Ames toxicity benchmark using a large-scale linear support vector machine. The results were promising and could often compete with literature results. On the large Ames benchmark, for example, we obtained an AUC ROC performance of 0.87 with a reimplementation of the extended connectivity fingerprint. This result is comparable to the performance achieved by a non-linear support vector machine using state-of-the-art descriptors. On the Sutherland QSAR data set, the best fingerprint encodings showed a comparable or better performance on 5 of the 8 benchmarks when compared against the results of the best descriptors published in the paper of Sutherland et al. Conclusions jCompoundMapper is a library for chemical graph fingerprints with several tweaking possibilities and exporting options for open source data mining toolkits. The quality of the data mining results, the conversion speed, the LPGL software license, the command-line interface, and the exporters should be useful for many applications in cheminformatics like benchmarks against literature methods, comparison of data mining algorithms, similarity searching, and similarity-based data mining. PMID:21219648

A Comparison of Video Modeling, Text-Based Instruction, and No Instruction for Creating Multiple Baseline Graphs in Microsoft Excel

ERIC Educational Resources Information Center

Tyner, Bryan C.; Fienup, Daniel M.

2015-01-01

Graphing is socially significant for behavior analysts; however, graphing can be difficult to learn. Video modeling (VM) may be a useful instructional method but lacks evidence for effective teaching of computer skills. A between-groups design compared the effects of VM, text-based instruction, and no instruction on graphing performance.…

LDRD final report :

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

Brost, Randolph C.; McLendon, William Clarence,

2013-01-01

Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report amore » preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.« less

FragariaCyc: A Metabolic Pathway Database for Woodland Strawberry Fragaria vesca

PubMed Central

Naithani, Sushma; Partipilo, Christina M.; Raja, Rajani; Elser, Justin L.; Jaiswal, Pankaj

2016-01-01

FragariaCyc is a strawberry-specific cellular metabolic network based on the annotated genome sequence of Fragaria vesca L. ssp. vesca, accession Hawaii 4. It was built on the Pathway-Tools platform using MetaCyc as the reference. The experimental evidences from published literature were used for supporting/editing existing entities and for the addition of new pathways, enzymes, reactions, compounds, and small molecules in the database. To date, FragariaCyc comprises 66 super-pathways, 488 unique pathways, 2348 metabolic reactions, 3507 enzymes, and 2134 compounds. In addition to searching and browsing FragariaCyc, researchers can compare pathways across various plant metabolic networks and analyze their data using Omics Viewer tool. We view FragariaCyc as a resource for the community of researchers working with strawberry and related fruit crops. It can help understanding the regulation of overall metabolism of strawberry plant during development and in response to diseases and abiotic stresses. FragariaCyc is available online at http://pathways.cgrb.oregonstate.edu. PMID:26973684

The shikimate pathway: review of amino acid sequence, function and three-dimensional structures of the enzymes.

PubMed

Mir, Rafia; Jallu, Shais; Singh, T P

2015-06-01

The aromatic compounds such as aromatic amino acids, vitamin K and ubiquinone are important prerequisites for the metabolism of an organism. All organisms can synthesize these aromatic metabolites through shikimate pathway, except for mammals which are dependent on their diet for these compounds. The pathway converts phosphoenolpyruvate and erythrose 4-phosphate to chorismate through seven enzymatically catalyzed steps and chorismate serves as a precursor for the synthesis of variety of aromatic compounds. These enzymes have shown to play a vital role for the viability of microorganisms and thus are suggested to present attractive molecular targets for the design of novel antimicrobial drugs. This review focuses on the seven enzymes of the shikimate pathway, highlighting their primary sequences, functions and three-dimensional structures. The understanding of their active site amino acid maps, functions and three-dimensional structures will provide a framework on which the rational design of antimicrobial drugs would be based. Comparing the full length amino acid sequences and the X-ray crystal structures of these enzymes from bacteria, fungi and plant sources would contribute in designing a specific drug and/or in developing broad-spectrum compounds with efficacy against a variety of pathogens.

In search of new lead compounds for trypanosomiasis drug design: A protein structure-based linked-fragment approach

NASA Astrophysics Data System (ADS)

Verlinde, Christophe L. M. J.; Rudenko, Gabrielle; Hol, Wim G. J.

1992-04-01

A modular method for pursuing structure-based inhibitor design in the framework of a design cycle is presented. The approach entails four stages: (1) a design pathway is defined in the three-dimensional structure of a target protein; (2) this pathway is divided into subregions; (3) complementary building blocks, also called fragments, are designed in each subregion; complementarity is defined in terms of shape, hydrophobicity, hydrogen bond properties and electrostatics; and (4) fragments from different subregions are linked into potential lead compounds. Stages (3) and (4) are qualitatively guided by force-field calculations. In addition, the designed fragments serve as entries for retrieving existing compounds from chemical databases. This linked-fragment approach has been applied in the design of potentially selective inhibitors of triosephosphate isomerase from Trypanosoma brucei, the causative agent of sleeping sickness.

cMapper: gene-centric connectivity mapper for EBI-RDF platform.

PubMed

Shoaib, Muhammad; Ansari, Adnan Ahmad; Ahn, Sung-Min

2017-01-15

In this era of biological big data, data integration has become a common task and a challenge for biologists. The Resource Description Framework (RDF) was developed to enable interoperability of heterogeneous datasets. The EBI-RDF platform enables an efficient data integration of six independent biological databases using RDF technologies and shared ontologies. However, to take advantage of this platform, biologists need to be familiar with RDF technologies and SPARQL query language. To overcome this practical limitation of the EBI-RDF platform, we developed cMapper, a web-based tool that enables biologists to search the EBI-RDF databases in a gene-centric manner without a thorough knowledge of RDF and SPARQL. cMapper allows biologists to search data entities in the EBI-RDF platform that are connected to genes or small molecules of interest in multiple biological contexts. The input to cMapper consists of a set of genes or small molecules, and the output are data entities in six independent EBI-RDF databases connected with the given genes or small molecules in the user's query. cMapper provides output to users in the form of a graph in which nodes represent data entities and the edges represent connections between data entities and inputted set of genes or small molecules. Furthermore, users can apply filters based on database, taxonomy, organ and pathways in order to focus on a core connectivity graph of their interest. Data entities from multiple databases are differentiated based on background colors. cMapper also enables users to investigate shared connections between genes or small molecules of interest. Users can view the output graph on a web browser or download it in either GraphML or JSON formats. cMapper is available as a web application with an integrated MySQL database. The web application was developed using Java and deployed on Tomcat server. We developed the user interface using HTML5, JQuery and the Cytoscape Graph API. cMapper can be accessed at http://cmapper.ewostech.net Readers can download the development manual from the website http://cmapper.ewostech.net/docs/cMapperDocumentation.pdf. Source Code is available at https://github.com/muhammadshoaib/cmapperContact:smahn@gachon.ac.krSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Data-based Decision-making: Teachers' Comprehension of Curriculum-based Measurement Progress-monitoring Graphs

ERIC Educational Resources Information Center

van den Bosch, Roxette M.; Espin, Christine A.; Chung, Siuman; Saab, Nadira

2017-01-01

Teachers have difficulty using data from Curriculum-based Measurement (CBM) progress graphs of students with learning difficulties for instructional decision-making. As a first step in unraveling those difficulties, we studied teachers' comprehension of CBM graphs. Using think-aloud methodology, we examined 23 teachers' ability to…

Key-Node-Separated Graph Clustering and Layouts for Human Relationship Graph Visualization.

PubMed

Itoh, Takayuki; Klein, Karsten

2015-01-01

Many graph-drawing methods apply node-clustering techniques based on the density of edges to find tightly connected subgraphs and then hierarchically visualize the clustered graphs. However, users may want to focus on important nodes and their connections to groups of other nodes for some applications. For this purpose, it is effective to separately visualize the key nodes detected based on adjacency and attributes of the nodes. This article presents a graph visualization technique for attribute-embedded graphs that applies a graph-clustering algorithm that accounts for the combination of connections and attributes. The graph clustering step divides the nodes according to the commonality of connected nodes and similarity of feature value vectors. It then calculates the distances between arbitrary pairs of clusters according to the number of connecting edges and the similarity of feature value vectors and finally places the clusters based on the distances. Consequently, the technique separates important nodes that have connections to multiple large clusters and improves the visibility of such nodes' connections. To test this technique, this article presents examples with human relationship graph datasets, including a coauthorship and Twitter communication network dataset.

A management-oriented framework for selecting metrics used to assess habitat- and path-specific quality in spatially structured populations

USGS Publications Warehouse

Nicol, Sam; Wiederholt, Ruscena; Diffendorfer, James E.; Mattsson, Brady; Thogmartin, Wayne E.; Semmens, Darius J.; Laura Lopez-Hoffman,; Norris, Ryan

2016-01-01

Mobile species with complex spatial dynamics can be difficult to manage because their population distributions vary across space and time, and because the consequences of managing particular habitats are uncertain when evaluated at the level of the entire population. Metrics to assess the importance of habitats and pathways connecting habitats in a network are necessary to guide a variety of management decisions. Given the many metrics developed for spatially structured models, it can be challenging to select the most appropriate one for a particular decision. To guide the management of spatially structured populations, we define three classes of metrics describing habitat and pathway quality based on their data requirements (graph-based, occupancy-based, and demographic-based metrics) and synopsize the ecological literature relating to these classes. Applying the first steps of a formal decision-making approach (problem framing, objectives, and management actions), we assess the utility of metrics for particular types of management decisions. Our framework can help managers with problem framing, choosing metrics of habitat and pathway quality, and to elucidate the data needs for a particular metric. Our goal is to help managers to narrow the range of suitable metrics for a management project, and aid in decision-making to make the best use of limited resources.

Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs.

PubMed

Allain, Ariane; Chauvot de Beauchêne, Isaure; Langenfeld, Florent; Guarracino, Yann; Laine, Elodie; Tchertanov, Luba

2014-01-01

Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach - MOdular NETwork Analysis (MONETA) - based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non-activated STAT5 proteins. Our theoretical prediction based on results obtained with MONETA was validated for KIT by in vitro experiments. MONETA is a versatile analytical and visualization tool entirely devoted to the understanding of the functioning/malfunctioning of allosteric regulation in proteins - a crucial basis to guide the discovery of next-generation allosteric drugs.

From data towards knowledge: revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data.

PubMed

Lu, Songjian; Jin, Bo; Cowart, L Ashley; Lu, Xinghua

2013-01-01

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal; and 3) revealing the architecture of a signaling system by organizing signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis has led to many new hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architecture of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which can be readily translated into computable knowledge in the form of rules regarding the yeast signaling system, such as "if genes involved in the MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed."

Designing overall stoichiometric conversions and intervening metabolic reactions

DOE PAGES

Chowdhury, Anupam; Maranas, Costas D.

2015-11-04

Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e.,more » minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C 2+ metabolites with higher carbon efficiency.« less

Molecularly imprinted polymers as a tool for the study of the 4-ethylphenol metabolic pathway in red wines.

PubMed

Garcia, Deiene; Gomez-Caballero, Alberto; Guerreiro, Antonio; Goicolea, M Aranzazu; Barrio, Ramon J

2015-09-04

A molecularly imprinted polymer (MIP) based methodology is described here for the determination of compounds that belong to the 4-ethylphenol (4EP) metabolic pathway in red wines. To this end, two MIP materials have been developed: a 4EP MIP as a class-selective material to extract phenols that belong to the 4EP metabolic pathway and a coumaric acid (CA) imprinted polymer as a MIP-based stationary phase capable of selectively separating these phenols on HPLC analysis, obtaining clean chromatograms. 4-vinyl pyridine and ethylene glycol dimethacrylate were respectively used as functional monomer and cross-linker for both MIPs. Once polymer compositions were optimised, the 4EP MIP was packed into SPE cartridges for wine sample clean-up and CA MIP was packed into HPLC columns to chromatographically separate the compounds present in the eluates obtained after SPE extraction. The accuracy of the proposed method was tested spiking wine samples with known concentrations of target compounds and subsequently, analytes were quantified by the standard addition method. Registered mean recoveries ranged from 95.2 to 109.2% and RSD values were below 10% in most cases. The described methodology was found to be suitable for the selective extraction and quantification of the compounds that belong to the 4EP metabolic pathway in red wines with minimal matrix effects and could be undoubtedly exploited to monitor 4EP and its precursors in wines. Copyright © 2015 Elsevier B.V. All rights reserved.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

DOEpatents

Brettin, Thomas S.; Cottingham, Robert W.; Griffith, Shelton D.; Quest, Daniel J.

2015-09-08

A system and method of integrating diverse sources of data and data streams is presented. The method can include selecting a scenario based on a topic, creating a multi-relational directed graph based on the scenario, identifying and converting resources in accordance with the scenario and updating the multi-directed graph based on the resources, identifying data feeds in accordance with the scenario and updating the multi-directed graph based on the data feeds, identifying analytical routines in accordance with the scenario and updating the multi-directed graph using the analytical routines and identifying data outputs in accordance with the scenario and defining queries to produce the data outputs from the multi-directed graph.

Electronic tongue for nitro and peroxide explosive sensing.

PubMed

González-Calabuig, Andreu; Cetó, Xavier; Del Valle, Manel

2016-06-01

This work reports the application of a voltammetric electronic tongue (ET) towards the simultaneous determination of both nitro-containing and peroxide-based explosive compounds, two families that represent the vast majority of compounds employed either in commercial mixtures or in improvised explosive devices. The multielectrode array was formed by graphite, gold and platinum electrodes, which exhibited marked mix-responses towards the compounds examined; namely, 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), pentaerythritol tetranitrate (PETN), 2,4,6-trinitrotoluene (TNT), N-methyl-N,2,4,6-tetranitroaniline (Tetryl) and triacetone triperoxide (TATP). Departure information was the set of voltammograms, which were first analyzed by means of principal component analysis (PCA) allowing the discrimination of the different individual compounds, while artificial neural networks (ANNs) were used for the resolution and individual quantification of some of their mixtures (total normalized root mean square error for the external test set of 0.108 and correlation of the obtained vs. expected concentrations comparison graphs r>0.929). Copyright © 2016 Elsevier B.V. All rights reserved.

Multi-label literature classification based on the Gene Ontology graph.

PubMed

Jin, Bo; Muller, Brian; Zhai, Chengxiang; Lu, Xinghua

2008-12-08

The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.

Hierarchical graphs for rule-based modeling of biochemical systems

PubMed Central

2011-01-01

Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR) complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for specifying rule-based models, such as the BioNetGen language (BNGL). Thus, the proposed use of hierarchical graphs should promote clarity and better understanding of rule-based models. PMID:21288338

Graph-Based Object Class Discovery

NASA Astrophysics Data System (ADS)

Xia, Shengping; Hancock, Edwin R.

We are interested in the problem of discovering the set of object classes present in a database of images using a weakly supervised graph-based framework. Rather than making use of the ”Bag-of-Features (BoF)” approach widely used in current work on object recognition, we represent each image by a graph using a group of selected local invariant features. Using local feature matching and iterative Procrustes alignment, we perform graph matching and compute a similarity measure. Borrowing the idea of query expansion , we develop a similarity propagation based graph clustering (SPGC) method. Using this method class specific clusters of the graphs can be obtained. Such a cluster can be generally represented by using a higher level graph model whose vertices are the clustered graphs, and the edge weights are determined by the pairwise similarity measure. Experiments are performed on a dataset, in which the number of images increases from 1 to 50K and the number of objects increases from 1 to over 500. Some objects have been discovered with total recall and a precision 1 in a single cluster.

Discrimination Power of Polynomial-Based Descriptors for Graphs by Using Functional Matrices.

PubMed

Dehmer, Matthias; Emmert-Streib, Frank; Shi, Yongtang; Stefu, Monica; Tripathi, Shailesh

2015-01-01

In this paper, we study the discrimination power of graph measures that are based on graph-theoretical matrices. The paper generalizes the work of [M. Dehmer, M. Moosbrugger. Y. Shi, Encoding structural information uniquely with polynomial-based descriptors by employing the Randić matrix, Applied Mathematics and Computation, 268(2015), 164-168]. We demonstrate that by using the new functional matrix approach, exhaustively generated graphs can be discriminated more uniquely than shown in the mentioned previous work.

Discrimination Power of Polynomial-Based Descriptors for Graphs by Using Functional Matrices

PubMed Central

Dehmer, Matthias; Emmert-Streib, Frank; Shi, Yongtang; Stefu, Monica; Tripathi, Shailesh

2015-01-01

In this paper, we study the discrimination power of graph measures that are based on graph-theoretical matrices. The paper generalizes the work of [M. Dehmer, M. Moosbrugger. Y. Shi, Encoding structural information uniquely with polynomial-based descriptors by employing the Randić matrix, Applied Mathematics and Computation, 268(2015), 164–168]. We demonstrate that by using the new functional matrix approach, exhaustively generated graphs can be discriminated more uniquely than shown in the mentioned previous work. PMID:26479495

Mathematical modeling of the malignancy of cancer using graph evolution.

PubMed

Gunduz-Demir, Cigdem

2007-10-01

We report a novel computational method based on graph evolution process to model the malignancy of brain cancer called glioma. In this work, we analyze the phases that a graph passes through during its evolution and demonstrate strong relation between the malignancy of cancer and the phase of its graph. From the photomicrographs of tissues, which are diagnosed as normal, low-grade cancerous and high-grade cancerous, we construct cell-graphs based on the locations of cells; we probabilistically generate an edge between every pair of cells depending on the Euclidean distance between them. For a cell-graph, we extract connectivity information including the properties of its connected components in order to analyze the phase of the cell-graph. Working with brain tissue samples surgically removed from 12 patients, we demonstrate that cell-graphs generated for different tissue types evolve differently and that they exhibit different phase properties, which distinguish a tissue type from another.

Helping Students Make Sense of Graphs: An Experimental Trial of SmartGraphs Software

ERIC Educational Resources Information Center

Zucker, Andrew; Kay, Rachel; Staudt, Carolyn

2014-01-01

Graphs are commonly used in science, mathematics, and social sciences to convey important concepts; yet students at all ages demonstrate difficulties interpreting graphs. This paper reports on an experimental study of free, Web-based software called SmartGraphs that is specifically designed to help students overcome their misconceptions regarding…

The connectome of the basal ganglia.

PubMed

Schmitt, Oliver; Eipert, Peter; Kettlitz, Richard; Leßmann, Felix; Wree, Andreas

2016-03-01

The basal ganglia of the laboratory rat consist of a few core regions that are specifically interconnected by efferents and afferents of the central nervous system. In nearly 800 reports of tract-tracing investigations the connectivity of the basal ganglia is documented. The readout of connectivity data and the collation of all the connections of these reports in a database allows to generate a connectome. The collation, curation and analysis of such a huge amount of connectivity data is a great challenge and has not been performed before (Bohland et al. PloS One 4:e7200, 2009) in large connectomics projects based on meta-analysis of tract-tracing studies. Here, the basal ganglia connectome of the rat has been generated and analyzed using the consistent cross-platform and generic framework neuroVIISAS. Several advances of this connectome meta-study have been made: the collation of laterality data, the network-analysis of connectivity strengths and the assignment of regions to a hierarchically organized terminology. The basal ganglia connectome offers differences in contralateral connectivity of motoric regions in contrast to other regions. A modularity analysis of the weighted and directed connectome produced a specific grouping of regions. This result indicates a correlation of structural and functional subsystems. As a new finding, significant reciprocal connections of specific network motifs in this connectome were detected. All three principal basal ganglia pathways (direct, indirect, hyperdirect) could be determined in the connectome. By identifying these pathways it was found that there exist many further equivalent pathways possessing the same length and mean connectivity weight as the principal pathways. Based on the connectome data it is unknown why an excitation pattern may prefer principal rather than other equivalent pathways. In addition to these new findings the local graph-theoretical features of regions of the connectome have been determined. By performing graph theoretical analyses it turns out that beside the caudate putamen further regions like the mesencephalic reticular formation, amygdaloid complex and ventral tegmental area are important nodes in the basal ganglia connectome. The connectome data of this meta-study of tract-tracing reports of the basal ganglia are available for further network studies, the integration into neocortical connectomes and further extensive investigations of the basal ganglia dynamics in population simulations.

Inhibition of the calcineurin pathway by two tannins, chebulagic acid and chebulanin, isolated from Harrisonia abyssinica Oliv.

PubMed

Lee, Won Jeong; Moon, Jae Sun; Kim, Sung In; Kim, Young Tae; Nash, Oyekanmi; Bahn, Yong-Sun; Kim, Sung Uk

2014-10-01

In order to discover and develop novel signaling inhibitors from plants, a screening system was established targeting the two-component system of Cryptococcus neoformans by using the wild type and a calcineurin mutant of C. neoformans, based on the counter-regulatory action of high-osmolarity glycerol (Hog1) mitogen-activated protein kinase and the calcineurin pathways in C. neoformans. Among 10,000 plant extracts, that from Harrisonia abyssinica Oliv. exhibited the most potent inhibitory activity against C. neoformans var. grubii H99 with fludioxonil. Bioassay-guided fractionation was used to isolate two bioactive compounds from H. abyssinica, and these compounds were identified as chebulagic acid and chebulanin using spectroscopic methods. These compounds specifically inhibited the calcineurin pathway in C. neoformans. Moreover, they exhibited potent antifungal activities against various human pathogenic fungi with minimum inhibitory concentrations ranging from 0.25 to over 64 µg/ml.

Organic matter in carbonaceous chondrites, planetary satellites, asteroids and comets

NASA Technical Reports Server (NTRS)

Cronin, John R.; Pizzarello, Sandra; Cruikshank, Dale P.

1988-01-01

A detailed review is given of the organic compounds found in carbonaceous chondrite meteorites, especially the Murchison meteorite, and detected spectroscopically in other solar-system objects. The chemical processes by which the organic compounds could have formed in the early solar system and the conditions required for these processes are discussed, taking into account the possible alteration of the compounds during the lifetime of the meteoroid. Also considered are the implications for prebiotic evolution and the origin of life. Diagrams, graphs, and tables of numerical data are provided.

Theoretical Bound of CRLB for Energy Efficient Technique of RSS-Based Factor Graph Geolocation

NASA Astrophysics Data System (ADS)

Kahar Aziz, Muhammad Reza; Heriansyah; Saputra, EfaMaydhona; Musa, Ardiansyah

2018-03-01

To support the increase of wireless geolocation development as the key of the technology in the future, this paper proposes theoretical bound derivation, i.e., Cramer Rao lower bound (CRLB) for energy efficient of received signal strength (RSS)-based factor graph wireless geolocation technique. The theoretical bound derivation is crucially important to evaluate whether the energy efficient technique of RSS-based factor graph wireless geolocation is effective as well as to open the opportunity to further innovation of the technique. The CRLB is derived in this paper by using the Fisher information matrix (FIM) of the main formula of the RSS-based factor graph geolocation technique, which is lied on the Jacobian matrix. The simulation result shows that the derived CRLB has the highest accuracy as a bound shown by its lowest root mean squared error (RMSE) curve compared to the RMSE curve of the RSS-based factor graph geolocation technique. Hence, the derived CRLB becomes the lower bound for the efficient technique of RSS-based factor graph wireless geolocation.

Probing the ToxCast Chemical Library for Predictive Signatures of Developmental Toxicity

EPA Science Inventory

EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

[Study on action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis based on techniques of gene expression profile and molecular fingerprint].

PubMed

Zhou, Wei; Song, Xiang-gang; Chen, Chao; Wang, Shu-mei; Liang, Sheng-wang

2015-08-01

Action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis were discussed based on gene expression profile and molecular fingerprint in this paper. First, gene expression profiles of atherosclerotic carotid artery tissues and histologically normal tissues in human body were collected, and were screened using significance analysis of microarray (SAM) to screen out differential gene expressions; then differential genes were analyzed by Gene Ontology (GO) analysis and KEGG pathway analysis; to avoid some genes with non-outstanding differential expression but biologically importance, Gene Set Enrichment Analysis (GSEA) were performed, and 7 chemical ingredients with higher negative enrichment score were obtained by Cmap method, implying that they could reversely regulate the gene expression profiles of pathological tissues; and last, based on the hypotheses that similar structures have similar activities, 336 ingredients of compound Danshen dripping pills were compared with 7 drug molecules in 2D molecular fingerprints method. The results showed that 147 differential genes including 60 up-regulated genes and 87 down regulated genes were screened out by SAM. And in GO analysis, Biological Process ( BP) is mainly concerned with biological adhesion, response to wounding and inflammatory response; Cellular Component (CC) is mainly concerned with extracellular region, extracellular space and plasma membrane; while Molecular Function (MF) is mainly concerned with antigen binding, metalloendopeptidase activity and peptide binding. KEGG pathway analysis is mainly concerned with JAK-STAT, RIG-I like receptor and PPAR signaling pathway. There were 10 compounds, such as hexadecane, with Tanimoto coefficients greater than 0.85, which implied that they may be the active ingredients (AIs) of compound Danshen dripping pills in treatment of carotid atherosclerosis (CAs). The present method can be applied to the research on material base and molecular action mechanism of TCM.

Building Knowledge Graphs for NASA's Earth Science Enterprise

NASA Astrophysics Data System (ADS)

Zhang, J.; Lee, T. J.; Ramachandran, R.; Shi, R.; Bao, Q.; Gatlin, P. N.; Weigel, A. M.; Maskey, M.; Miller, J. J.

2016-12-01

Inspired by Google Knowledge Graph, we have been building a prototype Knowledge Graph for Earth scientists, connecting information and data in NASA's Earth science enterprise. Our primary goal is to advance the state-of-the-art NASA knowledge extraction capability by going beyond traditional catalog search and linking different distributed information (such as data, publications, services, tools and people). This will enable a more efficient pathway to knowledge discovery. While Google Knowledge Graph provides impressive semantic-search and aggregation capabilities, it is limited to search topics for general public. We use the similar knowledge graph approach to semantically link information gathered from a wide variety of sources within the NASA Earth Science enterprise. Our prototype serves as a proof of concept on the viability of building an operational "knowledge base" system for NASA Earth science. Information is pulled from structured sources (such as NASA CMR catalog, GCMD, and Climate and Forecast Conventions) and unstructured sources (such as research papers). Leveraging modern techniques of machine learning, information retrieval, and deep learning, we provide an integrated data mining and information discovery environment to help Earth scientists to use the best data, tools, methodologies, and models available to answer a hypothesis. Our knowledge graph would be able to answer questions like: Which articles discuss topics investigating similar hypotheses? How have these methods been tested for accuracy? Which approaches have been highly cited within the scientific community? What variables were used for this method and what datasets were used to represent them? What processing was necessary to use this data? These questions then lead researchers and citizen scientists to investigate the sources where data can be found, available user guides, information on how the data was acquired, and available tools and models to use with this data. As a proof of concept, we focus on a well-defined domain - Hurricane Science linking research articles and their findings, data, people and tools/services. Modern information retrieval, natural language processing machine learning and deep learning techniques are applied to build the knowledge network.

Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

PubMed

Liu, Mengjin; Bienfait, Bruno; Sacher, Oliver; Gasteiger, Johann; Siezen, Roland J; Nauta, Arjen; Geurts, Jan M W

2014-01-01

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the "missing links" between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

Signal Processing for Time-Series Functions on a Graph

DTIC Science & Technology

2018-02-01

as filtering to functions supported on graphs. These methods can be applied to scalar functions with a domain that can be described by a fixed...classical signal processing such as filtering to account for the graph domain. This work essentially divides into 2 basic approaches: graph Laplcian...based filtering and weighted adjacency matrix-based filtering . In Shuman et al.,11 and elaborated in Bronstein et al.,13 filtering operators are

VitisCyc: a metabolic pathway knowledgebase for grapevine (Vitis vinifera)

PubMed Central

Naithani, Sushma; Raja, Rajani; Waddell, Elijah N.; Elser, Justin; Gouthu, Satyanarayana; Deluc, Laurent G.; Jaiswal, Pankaj

2014-01-01

We have developed VitisCyc, a grapevine-specific metabolic pathway database that allows researchers to (i) search and browse the database for its various components such as metabolic pathways, reactions, compounds, genes and proteins, (ii) compare grapevine metabolic networks with other publicly available plant metabolic networks, and (iii) upload, visualize and analyze high-throughput data such as transcriptomes, proteomes, metabolomes etc. using OMICs-Viewer tool. VitisCyc is based on the genome sequence of the nearly homozygous genotype PN40024 of Vitis vinifera “Pinot Noir” cultivar with 12X v1 annotations and was built on BioCyc platform using Pathway Tools software and MetaCyc reference database. Furthermore, VitisCyc was enriched for plant-specific pathways and grape-specific metabolites, reactions and pathways. Currently VitisCyc harbors 68 super pathways, 362 biosynthesis pathways, 118 catabolic pathways, 5 detoxification pathways, 36 energy related pathways and 6 transport pathways, 10,908 enzymes, 2912 enzymatic reactions, 31 transport reactions and 2024 compounds. VitisCyc, as a community resource, can aid in the discovery of candidate genes and pathways that are regulated during plant growth and development, and in response to biotic and abiotic stress signals generated from a plant's immediate environment. VitisCyc version 3.18 is available online at http://pathways.cgrb.oregonstate.edu. PMID:25538713

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics.

PubMed

Sharma, Harshita; Alekseychuk, Alexander; Leskovsky, Peter; Hellwich, Olaf; Anand, R S; Zerbe, Norman; Hufnagl, Peter

2012-10-04

Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics

PubMed Central

2012-01-01

Background Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. Methods The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. Results The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. Conclusion The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923. PMID:23035717

A Method for Finding Metabolic Pathways Using Atomic Group Tracking.

PubMed

Huang, Yiran; Zhong, Cheng; Lin, Hai Xiang; Wang, Jianyi

2017-01-01

A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways.

A Method for Finding Metabolic Pathways Using Atomic Group Tracking

PubMed Central

Zhong, Cheng; Lin, Hai Xiang; Wang, Jianyi

2017-01-01

A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways. PMID:28068354

On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis.

PubMed

Li, Bing; Chun, Hyonho; Zhao, Hongyu

2014-09-01

We introduce a nonparametric method for estimating non-gaussian graphical models based on a new statistical relation called additive conditional independence, which is a three-way relation among random vectors that resembles the logical structure of conditional independence. Additive conditional independence allows us to use one-dimensional kernel regardless of the dimension of the graph, which not only avoids the curse of dimensionality but also simplifies computation. It also gives rise to a parallel structure to the gaussian graphical model that replaces the precision matrix by an additive precision operator. The estimators derived from additive conditional independence cover the recently introduced nonparanormal graphical model as a special case, but outperform it when the gaussian copula assumption is violated. We compare the new method with existing ones by simulations and in genetic pathway analysis.

Construction and Optimization of a Heterologous Pathway for Protocatechuate Catabolism in Escherichia coli Enables Bioconversion of Model Aromatic Compounds

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

Clarkson, Sonya M.; Giannone, Richard J.; Kridelbaugh, Donna M.

The production of biofuels from lignocellulose yields a substantial lignin by-product stream that currently has few applications. Biological conversion of lignin-derived compounds into chemicals and fuels has the potential to improve the economics of lignocellulose-derived biofuels, but few microbes are able both to catabolize lignin-derived aromatic compounds and to generate valuable products. WhileEscherichia colihas been engineered to produce a variety of fuels and chemicals, it is incapable of catabolizing most aromatic compounds. Therefore, we engineeredE. colito catabolize protocatechuate, a common intermediate in lignin degradation, as the sole source of carbon and energy via heterologous expression of a nine-gene pathway fromPseudomonasmore » putidaKT2440. Then, we used experimental evolution to select for mutations that increased growth with protocatechuate more than 2-fold. Increasing the strength of a single ribosome binding site in the heterologous pathway was sufficient to recapitulate the increased growth. After optimization of the core pathway, we extended the pathway to enable catabolism of a second model compound, 4-hydroxybenzoate. These engineered strains will be useful platforms to discover, characterize, and optimize pathways for conversions of lignin-derived aromatics. IMPORTANCELignin is a challenging substrate for microbial catabolism due to its polymeric and heterogeneous chemical structure. Therefore, engineering microbes for improved catabolism of lignin-derived aromatic compounds will require the assembly of an entire network of catabolic reactions, including pathways from genetically intractable strains. By constructing defined pathways for aromatic compound degradation in a model host would allow rapid identification, characterization, and optimization of novel pathways. Finally, we constructed and optimized one such pathway inE. colito enable catabolism of a model aromatic compound, protocatechuate, and then extended the pathway to a related compound, 4-hydroxybenzoate. This optimized strain can now be used as the basis for the characterization of novel pathways.« less

Construction and Optimization of a Heterologous Pathway for Protocatechuate Catabolism in Escherichia coli Enables Bioconversion of Model Aromatic Compounds.

PubMed

Clarkson, Sonya M; Giannone, Richard J; Kridelbaugh, Donna M; Elkins, James G; Guss, Adam M; Michener, Joshua K

2017-09-15

The production of biofuels from lignocellulose yields a substantial lignin by-product stream that currently has few applications. Biological conversion of lignin-derived compounds into chemicals and fuels has the potential to improve the economics of lignocellulose-derived biofuels, but few microbes are able both to catabolize lignin-derived aromatic compounds and to generate valuable products. While Escherichia coli has been engineered to produce a variety of fuels and chemicals, it is incapable of catabolizing most aromatic compounds. Therefore, we engineered E. coli to catabolize protocatechuate, a common intermediate in lignin degradation, as the sole source of carbon and energy via heterologous expression of a nine-gene pathway from Pseudomonas putida KT2440. We next used experimental evolution to select for mutations that increased growth with protocatechuate more than 2-fold. Increasing the strength of a single ribosome binding site in the heterologous pathway was sufficient to recapitulate the increased growth. After optimization of the core pathway, we extended the pathway to enable catabolism of a second model compound, 4-hydroxybenzoate. These engineered strains will be useful platforms to discover, characterize, and optimize pathways for conversions of lignin-derived aromatics. IMPORTANCE Lignin is a challenging substrate for microbial catabolism due to its polymeric and heterogeneous chemical structure. Therefore, engineering microbes for improved catabolism of lignin-derived aromatic compounds will require the assembly of an entire network of catabolic reactions, including pathways from genetically intractable strains. Constructing defined pathways for aromatic compound degradation in a model host would allow rapid identification, characterization, and optimization of novel pathways. We constructed and optimized one such pathway in E. coli to enable catabolism of a model aromatic compound, protocatechuate, and then extended the pathway to a related compound, 4-hydroxybenzoate. This optimized strain can now be used as the basis for the characterization of novel pathways. Copyright © 2017 American Society for Microbiology.

Determining the semantic similarities among Gene Ontology terms.

PubMed

Taha, Kamal

2013-05-01

We present in this paper novel techniques that determine the semantic relationships among GeneOntology (GO) terms. We implemented these techniques in a prototype system called GoSE, which resides between user application and GO database. Given a set S of GO terms, GoSE would return another set S' of GO terms, where each term in S' is semantically related to each term in S. Most current research is focused on determining the semantic similarities among GO ontology terms based solely on their IDs and proximity to one another in the GO graph structure, while overlooking the contexts of the terms, which may lead to erroneous results. The context of a GO term T is the set of other terms, whose existence in the GO graph structure is dependent on T. We propose novel techniques that determine the contexts of terms based on the concept of existence dependency. We present a stack-based sort-merge algorithm employing these techniques for determining the semantic similarities among GO terms.We evaluated GoSE experimentally and compared it with three existing methods. The results of measuring the semantic similarities among genes in KEGG and Pfam pathways retrieved from the DBGET and Sanger Pfam databases, respectively, have shown that our method outperforms the other three methods in recall and precision.

Design and synthesis of a novel candidate compound NTI-007 targeting sodium taurocholate cotransporting polypeptide [NTCP]-APOA1-HBx-Beclin1-mediated autophagic pathway in HBV therapy.

PubMed

Zhang, Jin; Fu, Lei-Lei; Tian, Mao; Liu, Hao-Qiu; Li, Jing-Jing; Li, Yan; He, Jun; Huang, Jian; Ouyang, Liang; Gao, Hui-Yuan; Wang, Jin-Hui

2015-03-01

Sodium taurocholate cotransporting polypeptide (NTCP) is a multiple transmembrane transporter predominantly expressed in the liver, functioning as a functional receptor for HBV. Through our continuous efforts to identify NTCP as a novel HBV target, we designed and synthesized a series of new compounds based on the structure of our previous compound NT-5. Molecular docking and MD simulation validated that a new compound named NTI-007 can tightly bind to NTCP, whose efficacy was also measured in vitro virological examination and cytotoxicity studies. Furthermore, autophagy was observed in NTI-007 incubated HepG2.2.15 cells, and results of q-PCR and Western blotting revealed that NTI-007 induced autophagy through NTCP-APOA1-HBx-Beclin1-mediated pathway. Taken together, considering crucial role of NTCP in HBV infection, NTCP-mediated autophagic pathway may provide a promising strategy of HBV therapy and given efficacy of NTI-007 triggering autophagy. Our study suggests pre-clinical potential of this compound as a novel anti-HBV drug candidate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Efficient dynamic graph construction for inductive semi-supervised learning.

PubMed

Dornaika, F; Dahbi, R; Bosaghzadeh, A; Ruichek, Y

2017-10-01

Most of graph construction techniques assume a transductive setting in which the whole data collection is available at construction time. Addressing graph construction for inductive setting, in which data are coming sequentially, has received much less attention. For inductive settings, constructing the graph from scratch can be very time consuming. This paper introduces a generic framework that is able to make any graph construction method incremental. This framework yields an efficient and dynamic graph construction method that adds new samples (labeled or unlabeled) to a previously constructed graph. As a case study, we use the recently proposed Two Phase Weighted Regularized Least Square (TPWRLS) graph construction method. The paper has two main contributions. First, we use the TPWRLS coding scheme to represent new sample(s) with respect to an existing database. The representative coefficients are then used to update the graph affinity matrix. The proposed method not only appends the new samples to the graph but also updates the whole graph structure by discovering which nodes are affected by the introduction of new samples and by updating their edge weights. The second contribution of the article is the application of the proposed framework to the problem of graph-based label propagation using multiple observations for vision-based recognition tasks. Experiments on several image databases show that, without any significant loss in the accuracy of the final classification, the proposed dynamic graph construction is more efficient than the batch graph construction. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessing the impact of background spectral graph construction techniques on the topological anomaly detection algorithm

NASA Astrophysics Data System (ADS)

Ziemann, Amanda K.; Messinger, David W.; Albano, James A.; Basener, William F.

2012-06-01

Anomaly detection algorithms have historically been applied to hyperspectral imagery in order to identify pixels whose material content is incongruous with the background material in the scene. Typically, the application involves extracting man-made objects from natural and agricultural surroundings. A large challenge in designing these algorithms is determining which pixels initially constitute the background material within an image. The topological anomaly detection (TAD) algorithm constructs a graph theory-based, fully non-parametric topological model of the background in the image scene, and uses codensity to measure deviation from this background. In TAD, the initial graph theory structure of the image data is created by connecting an edge between any two pixel vertices x and y if the Euclidean distance between them is less than some resolution r. While this type of proximity graph is among the most well-known approaches to building a geometric graph based on a given set of data, there is a wide variety of dierent geometrically-based techniques. In this paper, we present a comparative test of the performance of TAD across four dierent constructs of the initial graph: mutual k-nearest neighbor graph, sigma-local graph for two different values of σ > 1, and the proximity graph originally implemented in TAD.

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

Zhang, Fangyan; Zhang, Song; Chung Wong, Pak

Effectively visualizing large graphs and capturing the statistical properties are two challenging tasks. To aid in these two tasks, many sampling approaches for graph simplification have been proposed, falling into three categories: node sampling, edge sampling, and traversal-based sampling. It is still unknown which approach is the best. We evaluate commonly used graph sampling methods through a combined visual and statistical comparison of graphs sampled at various rates. We conduct our evaluation on three graph models: random graphs, small-world graphs, and scale-free graphs. Initial results indicate that the effectiveness of a sampling method is dependent on the graph model, themore » size of the graph, and the desired statistical property. This benchmark study can be used as a guideline in choosing the appropriate method for a particular graph sampling task, and the results presented can be incorporated into graph visualization and analysis tools.« less

Use of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes (hiPSC-CMs) to Monitor Compound Effects on Cardiac Myocyte Signaling Pathways.

PubMed

Guo, Liang; Eldridge, Sandy; Furniss, Mike; Mussio, Jodie; Davis, Myrtle

2015-09-01

There is a need to develop mechanism-based assays to better inform risk of cardiotoxicity. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are rapidly gaining acceptance as a biologically relevant in vitro model for use in drug discovery and cardiotoxicity screens. Utilization of hiPSC-CMs for mechanistic investigations would benefit from confirmation of the expression and activity of cellular pathways that are known to regulate cardiac myocyte viability and function. This unit describes an approach to demonstrate the presence and function of signaling pathways in hiPSC-CMs and the effects of treatments on these pathways. We present a workflow that employs protocols to demonstrate protein expression and functional integrity of signaling pathway(s) of interest and to characterize biological consequences of signaling modulation. These protocols utilize a unique combination of structural, functional, and biochemical endpoints to interrogate compound effects on cardiomyocytes. Copyright © 2015 John Wiley & Sons, Inc.

Literature-based compound profiling: application to toxicogenomics.

PubMed

Frijters, Raoul; Verhoeven, Stefan; Alkema, Wynand; van Schaik, René; Polman, Jan

2007-11-01

To reduce continuously increasing costs in drug development, adverse effects of drugs need to be detected as early as possible in the process. In recent years, compound-induced gene expression profiling methodologies have been developed to assess compound toxicity, including Gene Ontology term and pathway over-representation analyses. The objective of this study was to introduce an additional approach, in which literature information is used for compound profiling to evaluate compound toxicity and mode of toxicity. Gene annotations were built by text mining in Medline abstracts for retrieval of co-publications between genes, pathology terms, biological processes and pathways. This literature information was used to generate compound-specific keyword fingerprints, representing over-represented keywords calculated in a set of regulated genes after compound administration. To see whether keyword fingerprints can be used for assessment of compound toxicity, we analyzed microarray data sets of rat liver treated with 11 hepatotoxicants. Analysis of keyword fingerprints of two genotoxic carcinogens, two nongenotoxic carcinogens, two peroxisome proliferators and two randomly generated gene sets, showed that each compound produced a specific keyword fingerprint that correlated with the experimentally observed histopathological events induced by the individual compounds. By contrast, the random sets produced a flat aspecific keyword profile, indicating that the fingerprints induced by the compounds reflect biological events rather than random noise. A more detailed analysis of the keyword profiles of diethylhexylphthalate, dimethylnitrosamine and methapyrilene (MPy) showed that the differences in the keyword fingerprints of these three compounds are based upon known distinct modes of action. Visualization of MPy-linked keywords and MPy-induced genes in a literature network enabled us to construct a mode of toxicity proposal for MPy, which is in agreement with known effects of MPy in literature. Compound keyword fingerprinting based on information retrieved from literature is a powerful approach for compound profiling, allowing evaluation of compound toxicity and analysis of the mode of action.

Probing the ToxCastTM Chemical Library for Predictive Signatures of Developmental Toxicity -NLTO Poster

EPA Science Inventory

EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

Building predictive models of developmental toxicity from ToxRefDB and ToxCast

EPA Science Inventory

EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that are highly correlated with observed in vivo toxicity. We hypothesize that cell signaling pathways underlying development are primary targets f...

Model-based morphological segmentation and labeling of coronary angiograms.

PubMed

Haris, K; Efstratiadis, S N; Maglaveras, N; Pappas, C; Gourassas, J; Louridas, G

1999-10-01

A method for extraction and labeling of the coronary arterial tree (CAT) using minimal user supervision in single-view angiograms is proposed. The CAT structural description (skeleton and borders) is produced, along with quantitative information for the artery dimensions and assignment of coded labels, based on a given coronary artery model represented by a graph. The stages of the method are: 1) CAT tracking and detection; 2) artery skeleton and border estimation; 3) feature graph creation; and iv) artery labeling by graph matching. The approximate CAT centerline and borders are extracted by recursive tracking based on circular template analysis. The accurate skeleton and borders of each CAT segment are computed, based on morphological homotopy modification and watershed transform. The approximate centerline and borders are used for constructing the artery segment enclosing area (ASEA), where the defined skeleton and border curves are considered as markers. Using the marked ASEA, an artery gradient image is constructed where all the ASEA pixels (except the skeleton ones) are assigned the gradient magnitude of the original image. The artery gradient image markers are imposed as its unique regional minima by the homotopy modification method, the watershed transform is used for extracting the artery segment borders, and the feature graph is updated. Finally, given the created feature graph and the known model graph, a graph matching algorithm assigns the appropriate labels to the extracted CAT using weighted maximal cliques on the association graph corresponding to the two given graphs. Experimental results using clinical digitized coronary angiograms are presented.

Nonschematic drawing recognition: a new approach based on attributed graph grammar with flexible embedding

NASA Astrophysics Data System (ADS)

Lee, Kyu J.; Kunii, T. L.; Noma, T.

1993-01-01

In this paper, we propose a syntactic pattern recognition method for non-schematic drawings, based on a new attributed graph grammar with flexible embedding. In our graph grammar, the embedding rule permits the nodes of a guest graph to be arbitrarily connected with the nodes of a host graph. The ambiguity caused by this flexible embedding is controlled with the evaluation of synthesized attributes and the check of context sensitivity. To integrate parsing with the synthesized attribute evaluation and the context sensitivity check, we also develop a bottom up parsing algorithm.

An experimental study of graph connectivity for unsupervised word sense disambiguation.

PubMed

Navigli, Roberto; Lapata, Mirella

2010-04-01

Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper, we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most "important" node among the set of graph nodes representing its senses. We introduce a graph-based WSD algorithm which has few parameters and does not require sense-annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets and show that our graph-based approach performs comparably to the state of the art.

Visual graph query formulation and exploration: a new perspective on information retrieval at the edge

NASA Astrophysics Data System (ADS)

Kase, Sue E.; Vanni, Michelle; Knight, Joanne A.; Su, Yu; Yan, Xifeng

2016-05-01

Within operational environments decisions must be made quickly based on the information available. Identifying an appropriate knowledge base and accurately formulating a search query are critical tasks for decision-making effectiveness in dynamic situations. The spreading of graph data management tools to access large graph databases is a rapidly emerging research area of potential benefit to the intelligence community. A graph representation provides a natural way of modeling data in a wide variety of domains. Graph structures use nodes, edges, and properties to represent and store data. This research investigates the advantages of information search by graph query initiated by the analyst and interactively refined within the contextual dimensions of the answer space toward a solution. The paper introduces SLQ, a user-friendly graph querying system enabling the visual formulation of schemaless and structureless graph queries. SLQ is demonstrated with an intelligence analyst information search scenario focused on identifying individuals responsible for manufacturing a mosquito-hosted deadly virus. The scenario highlights the interactive construction of graph queries without prior training in complex query languages or graph databases, intuitive navigation through the problem space, and visualization of results in graphical format.

Graph Drawing Aesthetics-Created by Users, Not Algorithms.

PubMed

Purchase, H C; Pilcher, C; Plimmer, B

2012-01-01

Prior empirical work on layout aesthetics for graph drawing algorithms has concentrated on the interpretation of existing graph drawings. We report on experiments which focus on the creation and layout of graph drawings: participants were asked to draw graphs based on adjacency lists, and to lay them out "nicely." Two interaction methods were used for creating the drawings: a sketch interface which allows for easy, natural hand movements, and a formal point-and-click interface similar to a typical graph editing system. We find, in common with many other studies, that removing edge crossings is the most significant aesthetic, but also discover that aligning nodes and edges to an underlying grid is important. We observe that the aesthetics favored by participants during creation of a graph drawing are often not evident in the final product and that the participants did not make a clear distinction between the processes of creation and layout. Our results suggest that graph drawing systems should integrate automatic layout with the user's manual editing process, and provide facilities to support grid-based graph creation.

Experimental and theoretical distribution of electron density and thermopolimerization in crystals of Ph3Sb(O2CCH=CH2)2 complex

NASA Astrophysics Data System (ADS)

Fukin, Georgy K.; Samsonov, Maxim A.; Arapova, Alla V.; Mazur, Anton S.; Artamonova, Tatiana O.; Khodorkovskiy, Mikhail A.; Vasilyev, Aleksander V.

2017-10-01

In this paper we present the results of a high-resolution single crystal X-ray diffraction experiment of a triphenylantimony diacrylate (Ph3Sb(O2CCH=CH2)2 (1)) and a subsequent charge density study based on a topological analysis according to quantum theory of atoms in molecules (QTAIM) together with density functional theory (DFT) calculation of isolated molecule. The QTAIM was used to investigate nature of the chemical bonds and molecular graph of Ph3Sb(O2CCH=CH2)2 complex. The molecular graph shows that only in one acrylate group there is an evidence of bonding between antimony and carbonyl oxygen atom in terms of the presence of a bond path. Thus the molecular graph for this class of compounds does not provide a definitive picture of the chemical bonding and should be complemented with other descriptors, such as and a source function (SF), noncovalent interaction (NCI) index and delocalization index (DI). Moreover the realization of π…π interactions between double bonds of acrylate groups in adjacent molecules allowed us to carry out a thermopolimerization reaction in crystals of Ph3Sb(O2CCH=CH2)2 complex and to determine a probable structure of polymer by solid state CP/MAS 13C NMR.

Analysis of the enzyme network involved in cattle milk production using graph theory.

PubMed

Ghorbani, Sholeh; Tahmoorespur, Mojtaba; Masoudi Nejad, Ali; Nasiri, Mohammad; Asgari, Yazdan

2015-06-01

Understanding cattle metabolism and its relationship with milk products is important in bovine breeding. A systemic view could lead to consequences that will result in a better understanding of existing concepts. Topological indices and quantitative characterizations mostly result from the application of graph theory on biological data. In the present work, the enzyme network involved in cattle milk production was reconstructed and analyzed based on available bovine genome information using several public datasets (NCBI, Uniprot, KEGG, and Brenda). The reconstructed network consisted of 3605 reactions named by KEGG compound numbers and 646 enzymes that catalyzed the corresponding reactions. The characteristics of the directed and undirected network were analyzed using Graph Theory. The mean path length was calculated to be4.39 and 5.41 for directed and undirected networks, respectively. The top 11 hub enzymes whose abnormality could harm bovine health and reduce milk production were determined. Therefore, the aim of constructing the enzyme centric network was twofold; first to find out whether such network followed the same properties of other biological networks, and second, to find the key enzymes. The results of the present study can improve our understanding of milk production in cattle. Also, analysis of the enzyme network can help improve the modeling and simulation of biological systems and help design desired phenotypes to increase milk production quality or quantity.

FTree query construction for virtual screening: a statistical analysis.

PubMed

Gerlach, Christof; Broughton, Howard; Zaliani, Andrea

2008-02-01

FTrees (FT) is a known chemoinformatic tool able to condense molecular descriptions into a graph object and to search for actives in large databases using graph similarity. The query graph is classically derived from a known active molecule, or a set of actives, for which a similar compound has to be found. Recently, FT similarity has been extended to fragment space, widening its capabilities. If a user were able to build a knowledge-based FT query from information other than a known active structure, the similarity search could be combined with other, normally separate, fields like de-novo design or pharmacophore searches. With this aim in mind, we performed a comprehensive analysis of several databases in terms of FT description and provide a basic statistical analysis of the FT spaces so far at hand. Vendors' catalogue collections and MDDR as a source of potential or known "actives", respectively, have been used. With the results reported herein, a set of ranges, mean values and standard deviations for several query parameters are presented in order to set a reference guide for the users. Applications on how to use this information in FT query building are also provided, using a newly built 3D-pharmacophore from 57 5HT-1F agonists and a published one which was used for virtual screening for tRNA-guanine transglycosylase (TGT) inhibitors.

FTree query construction for virtual screening: a statistical analysis

NASA Astrophysics Data System (ADS)

Gerlach, Christof; Broughton, Howard; Zaliani, Andrea

2008-02-01

FTrees (FT) is a known chemoinformatic tool able to condense molecular descriptions into a graph object and to search for actives in large databases using graph similarity. The query graph is classically derived from a known active molecule, or a set of actives, for which a similar compound has to be found. Recently, FT similarity has been extended to fragment space, widening its capabilities. If a user were able to build a knowledge-based FT query from information other than a known active structure, the similarity search could be combined with other, normally separate, fields like de-novo design or pharmacophore searches. With this aim in mind, we performed a comprehensive analysis of several databases in terms of FT description and provide a basic statistical analysis of the FT spaces so far at hand. Vendors' catalogue collections and MDDR as a source of potential or known "actives", respectively, have been used. With the results reported herein, a set of ranges, mean values and standard deviations for several query parameters are presented in order to set a reference guide for the users. Applications on how to use this information in FT query building are also provided, using a newly built 3D-pharmacophore from 57 5HT-1F agonists and a published one which was used for virtual screening for tRNA-guanine transglycosylase (TGT) inhibitors.

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

PubMed

Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut

2017-11-13

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Sketch Matching on Topology Product Graph.

PubMed

Liang, Shuang; Luo, Jun; Liu, Wenyin; Wei, Yichen

2015-08-01

Sketch matching is the fundamental problem in sketch based interfaces. After years of study, it remains challenging when there exists large irregularity and variations in the hand drawn sketch shapes. While most existing works exploit topology relations and graph representations for this problem, they are usually limited by the coarse topology exploration and heuristic (thus suboptimal) similarity metrics between graphs. We present a new sketch matching method with two novel contributions. We introduce a comprehensive definition of topology relations, which results in a rich and informative graph representation of sketches. For graph matching, we propose topology product graph that retains the full correspondence for matching two graphs. Based on it, we derive an intuitive sketch similarity metric whose exact solution is easy to compute. In addition, the graph representation and new metric naturally support partial matching, an important practical problem that received less attention in the literature. Extensive experimental results on a real challenging dataset and the superior performance of our method show that it outperforms the state-of-the-art.

Composing Data Parallel Code for a SPARQL Graph Engine

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

Castellana, Vito G.; Tumeo, Antonino; Villa, Oreste

Big data analytics process large amount of data to extract knowledge from them. Semantic databases are big data applications that adopt the Resource Description Framework (RDF) to structure metadata through a graph-based representation. The graph based representation provides several benefits, such as the possibility to perform in memory processing with large amounts of parallelism. SPARQL is a language used to perform queries on RDF-structured data through graph matching. In this paper we present a tool that automatically translates SPARQL queries to parallel graph crawling and graph matching operations. The tool also supports complex SPARQL constructs, which requires more than basicmore » graph matching for their implementation. The tool generates parallel code annotated with OpenMP pragmas for x86 Shared-memory Multiprocessors (SMPs). With respect to commercial database systems such as Virtuoso, our approach reduces memory occupation due to join operations and provides higher performance. We show the scaling of the automatically generated graph-matching code on a 48-core SMP.« less

Graphing Misconceptions and Possible Remedies Using Microcomputer-Based Labs.

ERIC Educational Resources Information Center

Barclay, William L.

Graphing is a common and powerful symbol system for representing concrete data. Yet research has shown that students often have graphical misconceptions about how graphs are related to the concrete event. Currently, the Technical Education Research Center (TERC) is developing microcomputer-based laboratories (MBL) science units that use probes to…

Graph Theory at the Service of Electroencephalograms.

PubMed

Iakovidou, Nantia D

2017-04-01

The brain is one of the largest and most complex organs in the human body and EEG is a noninvasive electrophysiological monitoring method that is used to record the electrical activity of the brain. Lately, the functional connectivity in human brain has been regarded and studied as a complex network using EEG signals. This means that the brain is studied as a connected system where nodes, or units, represent different specialized brain regions and links, or connections, represent communication pathways between the nodes. Graph theory and theory of complex networks provide a variety of measures, methods, and tools that can be useful to efficiently model, analyze, and study EEG networks. This article is addressed to computer scientists who wish to be acquainted and deal with the study of EEG data and also to neuroscientists who would like to become familiar with graph theoretic approaches and tools to analyze EEG data.

[Strategies of elucidation of biosynthetic pathways of natural products].

PubMed

Zou, Li-Qiu; Kuang, Xue-Jun; Sun, Chao; Chen, Shi-Lin

2016-11-01

Elucidation of the biosynthetic pathways of natural products is not only the major goal of herb genomics, but also the solid foundation of synthetic biology of natural products. Here, this paper reviewed recent advance in this field and put forward strategies to elucidate the biosynthetic pathway of natural products. Firstly, a proposed biosynthetic pathway should be set up based on well-known knowledge about chemical reactions and information on the identified compounds, as well as studies with isotope tracer. Secondly, candidate genes possibly involved in the biosynthetic pathway were screened out by co-expression analysis and/or gene cluster mining. Lastly, all the candidate genes were heterologously expressed in the host and then the enzyme involved in the biosynthetic pathway was characterized by activity assay. Sometimes, the function of the enzyme in the original plant could be further studied by RNAi or VIGS technology. Understanding the biosynthetic pathways of natural products will contribute to supply of new leading compounds by synthetic biology and provide "functional marker" for herbal molecular breeding, thus but boosting the development of traditional Chinese medicine agriculture. Copyright© by the Chinese Pharmaceutical Association.

Solving Graph Laplacian Systems Through Recursive Bisections and Two-Grid Preconditioning

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

Ponce, Colin; Vassilevski, Panayot S.

2016-02-18

We present a parallelizable direct method for computing the solution to graph Laplacian-based linear systems derived from graphs that can be hierarchically bipartitioned with small edge cuts. For a graph of size n with constant-size edge cuts, our method decomposes a graph Laplacian in time O(n log n), and then uses that decomposition to perform a linear solve in time O(n log n). We then use the developed technique to design a preconditioner for graph Laplacians that do not have this property. Finally, we augment this preconditioner with a two-grid method that accounts for much of the preconditioner's weaknesses. Wemore » present an analysis of this method, as well as a general theorem for the condition number of a general class of two-grid support graph-based preconditioners. Numerical experiments illustrate the performance of the studied methods.« less

Generic strategies for chemical space exploration.

PubMed

Andersen, Jakob L; Flamm, Christoph; Merkle, Daniel; Stadler, Peter F

2014-01-01

The chemical universe of molecules reachable from a set of start compounds by iterative application of a finite number of reactions is usually so vast, that sophisticated and efficient exploration strategies are required to cope with the combinatorial complexity. A stringent analysis of (bio)chemical reaction networks, as approximations of these complex chemical spaces, forms the foundation for the understanding of functional relations in Chemistry and Biology. Graphs and graph rewriting are natural models for molecules and reactions. Borrowing the idea of partial evaluation from functional programming, we introduce partial applications of rewrite rules. A framework for the specification of exploration strategies in graph-rewriting systems is presented. Using key examples of complex reaction networks from carbohydrate chemistry we demonstrate the feasibility of this high-level strategy framework. While being designed for chemical applications, the framework can also be used to emulate higher-level transformation models such as illustrated in a small puzzle game.

Self-organizing maps for learning the edit costs in graph matching.

PubMed

Neuhaus, Michel; Bunke, Horst

2005-06-01

Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively.

BioNetSim: a Petri net-based modeling tool for simulations of biochemical processes.

PubMed

Gao, Junhui; Li, Li; Wu, Xiaolin; Wei, Dong-Qing

2012-03-01

BioNetSim, a Petri net-based software for modeling and simulating biochemistry processes, is developed, whose design and implement are presented in this paper, including logic construction, real-time access to KEGG (Kyoto Encyclopedia of Genes and Genomes), and BioModel database. Furthermore, glycolysis is simulated as an example of its application. BioNetSim is a helpful tool for researchers to download data, model biological network, and simulate complicated biochemistry processes. Gene regulatory networks, metabolic pathways, signaling pathways, and kinetics of cell interaction are all available in BioNetSim, which makes modeling more efficient and effective. Similar to other Petri net-based softwares, BioNetSim does well in graphic application and mathematic construction. Moreover, it shows several powerful predominances. (1) It creates models in database. (2) It realizes the real-time access to KEGG and BioModel and transfers data to Petri net. (3) It provides qualitative analysis, such as computation of constants. (4) It generates graphs for tracing the concentration of every molecule during the simulation processes.

Fragmentation pathways of 2-substituted pyrrole derivatives using electrospray ionization ion trap and electrospray ionization quadrupole time-of-flight mass spectrometry.

PubMed

Liang, Xianrui; Guo, Zili; Yu, Chuanming

2013-10-30

Pyrrole derivatives are of considerable importance and are present in a wide range of natural products and used extensively in drug discovery. Fragmentation pathway studies play an important role in the structural identification of pyrrole derivatives. As a part of our ongoing work on heterocycles, fragmentation pathways of 2-substituted pyrrole derivatives were investigated by mass spectrometry (MS). Twelve pyrrole derivatives were synthesized and analyzed. Low-resolution fragmentation ions of all the compounds were generated by ion trap mass spectrometry (ITMS(n) ) with an electrospray ionization (ESI) source in positive mode. Hybrid quadrupole time-of-flight mass spectrometry (QTOFMS) was used to determine the elemental compositions of the resultant product ions. The side-chain substituents at the 2-position influence the fragmentation pathways. Typical losses of H2 O, aldehydes and pyrrole moieties from the [M + H](+) ion are observed for the compounds with side chains bearing aromatic groups at the 2-position of the pyrrole. However, losses of H2 O, alcohols and C3 H6 are the main cleavage pathways for compounds 6 and 12 with nonphenyl-substituted side chains at the 2-position. Typical fragmentation mechanisms of 2-substituted pyrrole derivatives are proposed and elucidated based on the observations of ITMS(n) and QTOFMS spectra. The results showed that the fragmentation pathways were remarkably influenced by the side-chain substituents at the 2-position of pyrrole. This investigation should have value in the structural identification of this series of molecules or compounds with similar structures. Copyright © 2013 John Wiley & Sons, Ltd.

Knotting fingerprints resolve knot complexity and knotting pathways in ideal knots.

PubMed

Hyde, David A B; Henrich, Joshua; Rawdon, Eric J; Millett, Kenneth C

2015-09-09

We use disk matrices to define knotting fingerprints that provide fine-grained insights into the local knotting structure of ideal knots. These knots have been found to have spatial properties that highly correlate with those of interesting macromolecules. From this fine structure and an analysis of the associated planar graph, one can define a measure of knot complexity using the number of independent unknotting pathways from the global knot type as the knot is trimmed progressively to a short arc unknot. A specialization of the Cheeger constant provides a measure of constraint on these independent unknotting pathways. Furthermore, the structure of the knotting fingerprint supports a comparison of the tight knot pathways to the unconstrained unknotting pathways of comparable length.

Synthesis and evaluation of (+)-decursin derivatives as inhibitors of the Wnt/β-catenin pathway.

PubMed

Lee, Jee-Hyun; Kim, Min-Ah; Park, Seoyoung; Cho, Soo-Hyun; Yun, Eunju; O, Yu-Seok; Kim, Jiseon; Goo, Ja-Il; Yun, Mi-Young; Choi, Yongseok; Oh, Sangtaek; Song, Gyu-Yong

2016-08-01

We synthesized (+)-decursin derivatives substituted with cinnamoyl- and phenyl propionyl groups originating from (+)-CGK062 and screened them using a cell-based assay to detect relative luciferase reporter activity. Of this series, compound 8b, in which a 3-acetoxy cinnamoyl group was introduced, most potently inhibited (97.0%) the Wnt/β-catenin pathway. Specifically, compound 8b dose-dependently inhibited Wnt3a-induced expression of the β-catenin response transcription (CRT) and increased β-catenin degradation in HEK293 reporter cells. Furthermore, compound 8b suppressed expression of the downstream β-catenin target genes cyclin D1 and c-myc and suppressed PC3 cell growth in a concentration-dependent manner. Copyright © 2016. Published by Elsevier Ltd.

A comparative study of theoretical graph models for characterizing structural networks of human brain.

PubMed

Li, Xiaojin; Hu, Xintao; Jin, Changfeng; Han, Junwei; Liu, Tianming; Guo, Lei; Hao, Wei; Li, Lingjiang

2013-01-01

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

Methods of visualizing graphs

DOEpatents

Wong, Pak C.; Mackey, Patrick S.; Perrine, Kenneth A.; Foote, Harlan P.; Thomas, James J.

2008-12-23

Methods for visualizing a graph by automatically drawing elements of the graph as labels are disclosed. In one embodiment, the method comprises receiving node information and edge information from an input device and/or communication interface, constructing a graph layout based at least in part on that information, wherein the edges are automatically drawn as labels, and displaying the graph on a display device according to the graph layout. In some embodiments, the nodes are automatically drawn as labels instead of, or in addition to, the label-edges.

A new intrusion prevention model using planning knowledge graph

NASA Astrophysics Data System (ADS)

Cai, Zengyu; Feng, Yuan; Liu, Shuru; Gan, Yong

2013-03-01

Intelligent plan is a very important research in artificial intelligence, which has applied in network security. This paper proposes a new intrusion prevention model base on planning knowledge graph and discuses the system architecture and characteristics of this model. The Intrusion Prevention based on plan knowledge graph is completed by plan recognition based on planning knowledge graph, and the Intrusion response strategies and actions are completed by the hierarchical task network (HTN) planner in this paper. Intrusion prevention system has the advantages of intelligent planning, which has the advantage of the knowledge-sharing, the response focused, learning autonomy and protective ability.

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

Biotransformation pathways of fluorotelomer-based polyfluoroalkyl substances: a review.

PubMed

Butt, Craig M; Muir, Derek C G; Mabury, Scott A

2014-02-01

The study reviews the current state of knowledge regarding the biotransformation of fluorotelomer-based compounds, with a focus on compounds that ultimately degrade to form perfluoroalkyl carboxylates (PFCAs). Most metabolism studies have been performed with either microbial systems or rats and mice, and comparatively few studies have used fish models. Furthermore, biotransformation studies thus far have predominately used the 8:2 fluorotelomer alcohol (FTOH) as the substrate. However, there have been an increasing number of studies investigating 6:2 FTOH biotransformation as a result of industry's transition to shorter-chain fluorotelomer chemistry. Studies with the 8:2 FTOH metabolism universally show the formation of perfluorooctanoate (PFOA) and, to a smaller fraction, perfluorononanoate (PFNA) and lower-chain-length PFCAs. In general, the overall yield of PFOA is low, presumably because of the multiple branches in the biotransformation pathways, including conjugation reactions in animal systems. There have been a few studies of non-FTOH biotransformation, which include polyfluoroalkyl phosphates (PAPs), 8:2 fluorotelomer acrylate (8:2 FTAC), and fluorotelomer carboxylates (FTCAs, FTUCAs). The PAPs compounds and 8:2 FTAC were shown to be direct precursors to FTOHs and thus follow similar degradation pathways. © 2013 SETAC.

Speech graphs provide a quantitative measure of thought disorder in psychosis.

PubMed

Mota, Natalia B; Vasconcelos, Nivaldo A P; Lemos, Nathalia; Pieretti, Ana C; Kinouchi, Osame; Cecchi, Guillermo A; Copelli, Mauro; Ribeiro, Sidarta

2012-01-01

Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.

Probing the ToxCastTM Chemical Library for Predictive Signatures of Developmental Toxicity - Poster at Teratology Society Annual Meeting

EPA Science Inventory

EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

Case-Based Plan Recognition Using Action Sequence Graphs

DTIC Science & Technology

2014-10-01

resized as necessary. Similarly, trace- based reasoning (Zarka et al., 2013) and episode -based reasoning (Sánchez-Marré, 2005) store fixed-length...is a goal state of Π, where satisfies has the same semantics as originally laid out in Ghallab, Nau & Traverso (2004). Action 0 is ...Although there are syntactic similarities between planning encoding graphs and action sequence graphs, important semantic differences exist because the

Enhancing Community Detection By Affinity-based Edge Weighting Scheme

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

Yoo, Andy; Sanders, Geoffrey; Henson, Van

Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is idealmore » for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.« less

Learning Mathematics with Interactive Whiteboards and Computer-Based Graphing Utility

ERIC Educational Resources Information Center

Erbas, Ayhan Kursat; Ince, Muge; Kaya, Sukru

2015-01-01

The purpose of this study was to explore the effect of a technology-supported learning environment utilizing an interactive whiteboard (IWB) and NuCalc graphing software compared to a traditional direct instruction-based environment on student achievement in graphs of quadratic functions and attitudes towards mathematics and technology. Sixty-five…

A genetic graph-based approach for partitional clustering.

PubMed

Menéndez, Héctor D; Barrero, David F; Camacho, David

2014-05-01

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

Supervoxels for graph cuts-based deformable image registration using guided image filtering

NASA Astrophysics Data System (ADS)

Szmul, Adam; Papież, Bartłomiej W.; Hallack, Andre; Grau, Vicente; Schnabel, Julia A.

2017-11-01

We propose combining a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for three-dimensional (3-D) deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to two-dimensional (2-D) applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation combined with graph cuts-based optimization can be applied to 3-D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model "sliding motion." Applying this method to lung image registration results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available computed tomography lung image dataset leads to the observation that our approach compares very favorably with state of the art methods in continuous and discrete image registration, achieving target registration error of 1.16 mm on average per landmark.

Supervoxels for Graph Cuts-Based Deformable Image Registration Using Guided Image Filtering.

PubMed

Szmul, Adam; Papież, Bartłomiej W; Hallack, Andre; Grau, Vicente; Schnabel, Julia A

2017-10-04

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model 'sliding motion'. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark.

Supervoxels for Graph Cuts-Based Deformable Image Registration Using Guided Image Filtering

PubMed Central

Szmul, Adam; Papież, Bartłomiej W.; Hallack, Andre; Grau, Vicente; Schnabel, Julia A.

2017-01-01

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model ‘sliding motion’. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark. PMID:29225433

Content-based image retrieval by matching hierarchical attributed region adjacency graphs

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Thies, Christian J.; Guld, Mark O.; Lehmann, Thomas M.

2004-05-01

Content-based image retrieval requires a formal description of visual information. In medical applications, all relevant biological objects have to be represented by this description. Although color as the primary feature has proven successful in publicly available retrieval systems of general purpose, this description is not applicable to most medical images. Additionally, it has been shown that global features characterizing the whole image do not lead to acceptable results in the medical context or that they are only suitable for specific applications. For a general purpose content-based comparison of medical images, local, i.e. regional features that are collected on multiple scales must be used. A hierarchical attributed region adjacency graph (HARAG) provides such a representation and transfers image comparison to graph matching. However, building a HARAG from an image requires a restriction in size to be computationally feasible while at the same time all visually plausible information must be preserved. For this purpose, mechanisms for the reduction of the graph size are presented. Even with a reduced graph, the problem of graph matching remains NP-complete. In this paper, the Similarity Flooding approach and Hopfield-style neural networks are adapted from the graph matching community to the needs of HARAG comparison. Based on synthetic image material build from simple geometric objects, all visually similar regions were matched accordingly showing the framework's general applicability to content-based image retrieval of medical images.

On understanding nuclear reaction network flows with branchings on directed graphs

NASA Astrophysics Data System (ADS)

Meyer, Bradley S.

2018-04-01

Nuclear reaction network flow diagrams are useful for understanding which reactions are governing the abundance changes at a particular time during nucleosynthesis. This is especially true when the flows are largely unidirectional, such as during the s-process of nucleosynthesis. In explosive nucleosynthesis, when reaction flows are large, and when forward reactions are nearly balanced by their reverses, reaction flows no longer give a clear picture of the abundance evolution in the network. This paper presents a way of understanding network evolution in terms of sums of branchings on a directed graph, which extends the concept of reaction flows to allow for multiple reaction pathways.

MISAGA: An Algorithm for Mining Interesting Subgraphs in Attributed Graphs.

PubMed

He, Tiantian; Chan, Keith C C

2018-05-01

An attributed graph contains vertices that are associated with a set of attribute values. Mining clusters or communities, which are interesting subgraphs in the attributed graph is one of the most important tasks of graph analytics. Many problems can be defined as the mining of interesting subgraphs in attributed graphs. Algorithms that discover subgraphs based on predefined topologies cannot be used to tackle these problems. To discover interesting subgraphs in the attributed graph, we propose an algorithm called mining interesting subgraphs in attributed graph algorithm (MISAGA). MISAGA performs its tasks by first using a probabilistic measure to determine whether the strength of association between a pair of attribute values is strong enough to be interesting. Given the interesting pairs of attribute values, then the degree of association is computed for each pair of vertices using an information theoretic measure. Based on the edge structure and degree of association between each pair of vertices, MISAGA identifies interesting subgraphs by formulating it as a constrained optimization problem and solves it by identifying the optimal affiliation of subgraphs for the vertices in the attributed graph. MISAGA has been tested with several large-sized real graphs and is found to be potentially very useful for various applications.

A distributed query execution engine of big attributed graphs.

PubMed

Batarfi, Omar; Elshawi, Radwa; Fayoumi, Ayman; Barnawi, Ahmed; Sakr, Sherif

2016-01-01

A graph is a popular data model that has become pervasively used for modeling structural relationships between objects. In practice, in many real-world graphs, the graph vertices and edges need to be associated with descriptive attributes. Such type of graphs are referred to as attributed graphs. G-SPARQL has been proposed as an expressive language, with a centralized execution engine, for querying attributed graphs. G-SPARQL supports various types of graph querying operations including reachability, pattern matching and shortest path where any G-SPARQL query may include value-based predicates on the descriptive information (attributes) of the graph edges/vertices in addition to the structural predicates. In general, a main limitation of centralized systems is that their vertical scalability is always restricted by the physical limits of computer systems. This article describes the design, implementation in addition to the performance evaluation of DG-SPARQL, a distributed, hybrid and adaptive parallel execution engine of G-SPARQL queries. In this engine, the topology of the graph is distributed over the main memory of the underlying nodes while the graph data are maintained in a relational store which is replicated on the disk of each of the underlying nodes. DG-SPARQL evaluates parts of the query plan via SQL queries which are pushed to the underlying relational stores while other parts of the query plan, as necessary, are evaluated via indexless memory-based graph traversal algorithms. Our experimental evaluation shows the efficiency and the scalability of DG-SPARQL on querying massive attributed graph datasets in addition to its ability to outperform the performance of Apache Giraph, a popular distributed graph processing system, by orders of magnitudes.

Development of a High-Throughput Gene Expression Screen for Modulators of RAS-MAPK Signaling in a Mutant RAS Cellular Context.

PubMed

Severyn, Bryan; Nguyen, Thi; Altman, Michael D; Li, Lixia; Nagashima, Kumiko; Naumov, George N; Sathyanarayanan, Sriram; Cook, Erica; Morris, Erick; Ferrer, Marc; Arthur, Bill; Benita, Yair; Watters, Jim; Loboda, Andrey; Hermes, Jeff; Gilliland, D Gary; Cleary, Michelle A; Carroll, Pamela M; Strack, Peter; Tudor, Matt; Andersen, Jannik N

2016-10-01

The RAS-MAPK pathway controls many cellular programs, including cell proliferation, differentiation, and apoptosis. In colorectal cancers, recurrent mutations in this pathway often lead to increased cell signaling that may contribute to the development of neoplasms, thereby making this pathway attractive for therapeutic intervention. To this end, we developed a 26-member gene signature of RAS-MAPK pathway activity utilizing the Affymetrix QuantiGene Plex 2.0 reagent system and performed both primary and confirmatory gene expression-based high-throughput screens (GE-HTSs) using KRAS mutant colon cancer cells (SW837) and leveraging a highly annotated chemical library. The screen achieved a hit rate of 1.4% and was able to enrich for hit compounds that target RAS-MAPK pathway members such as MEK and EGFR. Sensitivity and selectivity performance measurements were 0.84 and 1.00, respectively, indicating high true-positive and true-negative rates. Active compounds from the primary screen were confirmed in a dose-response GE-HTS assay, a GE-HTS assay using 14 additional cancer cell lines, and an in vitro colony formation assay. Altogether, our data suggest that this GE-HTS assay will be useful for larger unbiased chemical screens to identify novel compounds and mechanisms that may modulate the RAS-MAPK pathway. © 2016 Society for Laboratory Automation and Screening.

General low-temperature reaction pathway from precursors to monomers before nucleation of compound semiconductor nanocrystals

PubMed Central

Yu, Kui; Liu, Xiangyang; Qi, Ting; Yang, Huaqing; Whitfield, Dennis M.; Y. Chen, Queena; Huisman, Erik J. C.; Hu, Changwei

2016-01-01

Little is known about the molecular pathway to monomers of semiconductor nanocrystals. Here we report a general reaction pathway, which is based on hydrogen-mediated ligand loss for the precursor conversion to ‘monomers' at low temperature before nucleation. We apply 31P nuclear magnetic resonance spectroscopy to monitor the key phosphorous-containing products that evolve from MXn+E=PPh2H+HY mixtures, where MXn, E=PPh2H, and HY are metal precursors, chalcogenide precursors, and additives, respectively. Surprisingly, the phosphorous-containing products detected can be categorized into two groups, Ph2P–Y and Ph2P(E)–Y. On the basis of our experimental and theoretical results, we propose two competing pathways to the formation of M2En monomers, each of which is accompanied by one of the two products. Our study unravels the pathway of precursor evolution into M2En monomers, the stoichiometry of which directly correlates with the atomic composition of the final compound nanocrystals. PMID:27531507

A note on the stability and discriminability of graph-based features for classification problems in digital pathology

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Xu, Jun; Madabhushi, Anant

2015-01-01

Nuclear architecture or the spatial arrangement of individual cancer nuclei on histopathology images has been shown to be associated with different grades and differential risk for a number of solid tumors such as breast, prostate, and oropharyngeal. Graph-based representations of individual nuclei (nuclei representing the graph nodes) allows for mining of quantitative metrics to describe tumor morphology. These graph features can be broadly categorized into global and local depending on the type of graph construction method. While a number of local graph (e.g. Cell Cluster Graphs) and global graph (e.g. Voronoi, Delaunay Triangulation, Minimum Spanning Tree) features have been shown to associated with cancer grade, risk, and outcome for different cancer types, the sensitivity of the preceding segmentation algorithms in identifying individual nuclei can have a significant bearing on the discriminability of the resultant features. This therefore begs the question as to which features while being discriminative of cancer grade and aggressiveness are also the most resilient to the segmentation errors. These properties are particularly desirable in the context of digital pathology images, where the method of slide preparation, staining, and type of nuclear segmentation algorithm employed can all dramatically affect the quality of the nuclear graphs and corresponding features. In this paper we evaluated the trade off between discriminability and stability of both global and local graph-based features in conjunction with a few different segmentation algorithms and in the context of two different histopathology image datasets of breast cancer from whole-slide images (WSI) and tissue microarrays (TMA). Specifically in this paper we investigate a few different performance measures including stability, discriminability and stability vs discriminability trade off, all of which are based on p-values from the Kruskal-Wallis one-way analysis of variance for local and global graph features. Apart from identifying the set of local and global features that satisfied the trade off between stability and discriminability, our most interesting finding was that a simple segmentation method was sufficient to identify the most discriminant features for invasive tumour detection in TMAs, whereas for tumour grading in WSI, the graph based features were more sensitive to the accuracy of the segmentation algorithm employed.

Biotechnological production of aromatic compounds of the extended shikimate pathway from renewable biomass.

PubMed

Lee, Jin-Ho; Wendisch, Volker F

2017-09-10

Aromatic chemicals that contain an unsaturated ring with alternating double and single bonds find numerous applications in a wide range of industries, e.g. paper and dye manufacture, as fuel additives, electrical insulation, resins, pharmaceuticals, agrochemicals, in food, feed and cosmetics. Their chemical production is based on petroleum (BTX; benzene, toluene, and xylene), but they can also be obtained from plants by extraction. Due to petroleum depletion, health compliance, or environmental issues such as global warming, the biotechnological production of aromatics from renewable biomass came more and more into focus. Lignin, a complex polymeric aromatic molecule itself, is a natural source of aromatic compounds. Many microorganisms are able to catabolize a plethora of aromatic compounds and interception of these pathways may lead to the biotechnological production of value-added aromatic compounds which will be discussed for Corynebacterium glutamicum. Biosynthesis of aromatic amino acids not only gives rise to l-tryptophan, L-tyrosine and l-phenylalanine, but also to aromatic intermediates such as dehydroshikimate or chorismate from which value-added aromatic compounds can be derived. In this review, we will summarize recent strategies for the biotechnological production of aromatic and related compounds from renewable biomass by Escherichia coli, Pseudomonas putida, C. glutamicum and Saccharomyces cerevisiae. In particular, we will focus on metabolic engineering of the extended shikimate pathway. Copyright © 2016 Elsevier B.V. All rights reserved.

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

PubMed Central

Lex, Alexander; Partl, Christian; Kalkofen, Denis; Streit, Marc; Gratzl, Samuel; Wassermann, Anne Mai; Schmalstieg, Dieter; Pfister, Hanspeter

2014-01-01

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types. Fig. 1Entourage showing the Glioma pathway in detail and contextual information of multiple related pathways. PMID:24051820

Biometric Subject Verification Based on Electrocardiographic Signals

NASA Technical Reports Server (NTRS)

Dusan, Sorin V. (Inventor); Jorgensen, Charles C. (Inventor)

2014-01-01

A method of authenticating or declining to authenticate an asserted identity of a candidate-person. In an enrollment phase, a reference PQRST heart action graph is provided or constructed from information obtained from a plurality of graphs that resemble each other for a known reference person, using a first graph comparison metric. In a verification phase, a candidate-person asserts his/her identity and presents a plurality of his/her heart cycle graphs. If a sufficient number of the candidate-person's measured graphs resemble each other, a representative composite graph is constructed from the candidate-person's graphs and is compared with a composite reference graph, for the person whose identity is asserted, using a second graph comparison metric. When the second metric value lies in a selected range, the candidate-person's assertion of identity is accepted.

EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU

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

Sengupta, Dipanjan; Song, Shuaiwen

With the prevalence of the World Wide Web and social networks, there has been a growing interest in high performance analytics for constantly-evolving dynamic graphs. Modern GPUs provide massive AQ1 amount of parallelism for efficient graph processing, but the challenges remain due to their lack of support for the near real-time streaming nature of dynamic graphs. Specifically, due to the current high volume and velocity of graph data combined with the complexity of user queries, traditional processing methods by first storing the updates and then repeatedly running static graph analytics on a sequence of versions or snapshots are deemed undesirablemore » and computational infeasible on GPU. We present EvoGraph, a highly efficient and scalable GPU- based dynamic graph analytics framework.« less

Cytidine derivatives as IspF inhibitors of Burkolderia pseudomallei

PubMed Central

Zhang, Zheng; Jakkaraju, Sriram; Blain, Joy; Gogol, Kenneth; Zhao, Lei; Hartley, Robert C.; Karlsson, Courtney A.; Staker, Bart L.; Stewart, Lance J.; Myler, Peter J.; Clare, Michael; Begley, Darren W.; Horn, James R.; Hagen, Timothy J

2013-01-01

Published biological data suggest that the methyl erythritol phosphate (MEP) pathway, a non-mevalonate isoprenoid biosynthetic pathway, is essential for certain bacteria and other infectious disease organisms. One highly conserved enzyme in the MEP pathway is 2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase (IspF). Fragment-bound complexes of IspF from Burkholderia pseudomallei were used to design and synthesize a series of molecules linking the cytidine moiety to different zinc pocket fragment binders. Testing by surface plasmon resonance (SPR) found one molecule in the series to possess binding affinity equal to that of cytidine diphosphate, despite lacking any metal-coordinating phosphate groups. Close inspection of the SPR data suggest different binding stoichiometries between IspF and test compounds. Crystallographic analysis shows important variations between the binding mode of one synthesized compound and the pose of the bound fragment from which it was designed. The binding modes of these molecules add to our structural knowledge base for IspF and suggest future refinements in this compound series. PMID:24157367

Recursive Feature Extraction in Graphs

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

2014-08-14

ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

Structural and functional characterization of solute binding proteins for aromatic compounds derived from lignin: p-coumaric acid and related aromatic acids.

PubMed

Tan, Kemin; Chang, Changsoo; Cuff, Marianne; Osipiuk, Jerzy; Landorf, Elizabeth; Mack, Jamey C; Zerbs, Sarah; Joachimiak, Andrzej; Collart, Frank R

2013-10-01

Lignin comprises 15-25% of plant biomass and represents a major environmental carbon source for utilization by soil microorganisms. Access to this energy resource requires the action of fungal and bacterial enzymes to break down the lignin polymer into a complex assortment of aromatic compounds that can be transported into the cells. To improve our understanding of the utilization of lignin by microorganisms, we characterized the molecular properties of solute binding proteins of ATP-binding cassette transporter proteins that interact with these compounds. A combination of functional screens and structural studies characterized the binding specificity of the solute binding proteins for aromatic compounds derived from lignin such as p-coumarate, 3-phenylpropionic acid and compounds with more complex ring substitutions. A ligand screen based on thermal stabilization identified several binding protein clusters that exhibit preferences based on the size or number of aromatic ring substituents. Multiple X-ray crystal structures of protein-ligand complexes for these clusters identified the molecular basis of the binding specificity for the lignin-derived aromatic compounds. The screens and structural data provide new functional assignments for these solute-binding proteins which can be used to infer their transport specificity. This knowledge of the functional roles and molecular binding specificity of these proteins will support the identification of the specific enzymes and regulatory proteins of peripheral pathways that funnel these compounds to central metabolic pathways and will improve the predictive power of sequence-based functional annotation methods for this family of proteins. Copyright © 2013 Wiley Periodicals, Inc.

Structural and functional characterization of solute binding proteins for aromatic compounds derived from lignin: p-coumaric acid and related aromatic acids

PubMed Central

Tan, Kemin; Chang, Changsoo; Cuff, Marianne; Osipiuk, Jerzy; Landorf, Elizabeth; Mack, Jamey C.; Zerbs, Sarah; Joachimiak, Andrzej; Collart, Frank R.

2013-01-01

Lignin comprises 15.25% of plant biomass and represents a major environmental carbon source for utilization by soil microorganisms. Access to this energy resource requires the action of fungal and bacterial enzymes to break down the lignin polymer into a complex assortment of aromatic compounds that can be transported into the cells. To improve our understanding of the utilization of lignin by microorganisms, we characterized the molecular properties of solute binding proteins of ATP.binding cassette transporter proteins that interact with these compounds. A combination of functional screens and structural studies characterized the binding specificity of the solute binding proteins for aromatic compounds derived from lignin such as p-coumarate, 3-phenylpropionic acid and compounds with more complex ring substitutions. A ligand screen based on thermal stabilization identified several binding protein clusters that exhibit preferences based on the size or number of aromatic ring substituents. Multiple X-ray crystal structures of protein-ligand complexes for these clusters identified the molecular basis of the binding specificity for the lignin-derived aromatic compounds. The screens and structural data provide new functional assignments for these solute.binding proteins which can be used to infer their transport specificity. This knowledge of the functional roles and molecular binding specificity of these proteins will support the identification of the specific enzymes and regulatory proteins of peripheral pathways that funnel these compounds to central metabolic pathways and will improve the predictive power of sequence-based functional annotation methods for this family of proteins. PMID:23606130

Attribute-based Decision Graphs: A framework for multiclass data classification.

PubMed

Bertini, João Roberto; Nicoletti, Maria do Carmo; Zhao, Liang

2017-01-01

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances. Aiming at exploring alternative ways to build graphs from data, this paper proposes an algorithm for constructing a new type of graph, called Attribute-based Decision Graph-AbDG. Given a vector-based data set, an AbDG is built by partitioning each data attribute range into disjoint intervals and representing each interval as a vertex. The edges are then established between vertices from different attributes according to a pre-defined pattern. Classification is performed through a matching process among the attribute values of the new instance and AbDG. Moreover, AbDG provides an inner mechanism to handle missing attribute values, which contributes for expanding its applicability. Results of classification tasks have shown that AbDG is a competitive approach when compared to well-known multiclass algorithms. The main contribution of the proposed framework is the combination of the advantages of attribute-based and graph-based techniques to perform robust pattern matching data classification, while permitting the analysis the input data considering only a subset of its attributes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computers and the Rational-Root Theorem--Another View.

ERIC Educational Resources Information Center

Waits, Bert K.; Demana, Franklin

1989-01-01

An approach to finding the rational roots of polynomial equations based on computer graphing is given. It integrates graphing with the purely algebraic approach. Either computers or graphing calculators can be used. (MNS)

Fixation probability on clique-based graphs

NASA Astrophysics Data System (ADS)

Choi, Jeong-Ok; Yu, Unjong

2018-02-01

The fixation probability of a mutant in the evolutionary dynamics of Moran process is calculated by the Monte-Carlo method on a few families of clique-based graphs. It is shown that the complete suppression of fixation can be realized with the generalized clique-wheel graph in the limit of small wheel-clique ratio and infinite size. The family of clique-star is an amplifier, and clique-arms graph changes from amplifier to suppressor as the fitness of the mutant increases. We demonstrate that the overall structure of a graph can be more important to determine the fixation probability than the degree or the heat heterogeneity. The dependence of the fixation probability on the position of the first mutant is discussed.

Exploring astrobiology using in silico molecular structure generation.

PubMed

Meringer, Markus; Cleaves, H James

2017-12-28

The origin of life is typically understood as a transition from inanimate or disorganized matter to self-organized, 'animate' matter. This transition probably took place largely in the context of organic compounds, and most approaches, to date, have focused on using the organic chemical composition of modern organisms as the main guide for understanding this process. However, it has gradually come to be appreciated that biochemistry, as we know it, occupies a minute volume of the possible organic 'chemical space'. As the majority of abiotic syntheses appear to make a large set of compounds not found in biochemistry, as well as an incomplete subset of those that are, it is possible that life began with a significantly different set of components. Chemical graph-based structure generation methods allow for exhaustive in silico enumeration of different compound types and different types of 'chemical spaces' beyond those used by biochemistry, which can be explored to help understand the types of compounds biology uses, as well as to understand the nature of abiotic synthesis, and potentially design novel types of living systems.This article is part of the themed issue 'Reconceptualizing the origins of life'. © 2017 The Authors.

Exploring astrobiology using in silico molecular structure generation

NASA Astrophysics Data System (ADS)

Meringer, Markus; Cleaves, H. James

2017-11-01

The origin of life is typically understood as a transition from inanimate or disorganized matter to self-organized, `animate' matter. This transition probably took place largely in the context of organic compounds, and most approaches, to date, have focused on using the organic chemical composition of modern organisms as the main guide for understanding this process. However, it has gradually come to be appreciated that biochemistry, as we know it, occupies a minute volume of the possible organic `chemical space'. As the majority of abiotic syntheses appear to make a large set of compounds not found in biochemistry, as well as an incomplete subset of those that are, it is possible that life began with a significantly different set of components. Chemical graph-based structure generation methods allow for exhaustive in silico enumeration of different compound types and different types of `chemical spaces' beyond those used by biochemistry, which can be explored to help understand the types of compounds biology uses, as well as to understand the nature of abiotic synthesis, and potentially design novel types of living systems. This article is part of the themed issue 'Reconceptualizing the origins of life'.

Fitchi: haplotype genealogy graphs based on the Fitch algorithm.

PubMed

Matschiner, Michael

2016-04-15

: In population genetics and phylogeography, haplotype genealogy graphs are important tools for the visualization of population structure based on sequence data. In this type of graph, node sizes are often drawn in proportion to haplotype frequencies and edge lengths represent the minimum number of mutations separating adjacent nodes. I here present Fitchi, a new program that produces publication-ready haplotype genealogy graphs based on the Fitch algorithm. http://www.evoinformatics.eu/fitchi.htm : michaelmatschiner@mac.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

GraphMeta: Managing HPC Rich Metadata in Graphs

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

Dai, Dong; Chen, Yong; Carns, Philip

High-performance computing (HPC) systems face increasingly critical metadata management challenges, especially in the approaching exascale era. These challenges arise not only from exploding metadata volumes, but also from increasingly diverse metadata, which contains data provenance and arbitrary user-defined attributes in addition to traditional POSIX metadata. This ‘rich’ metadata is becoming critical to supporting advanced data management functionality such as data auditing and validation. In our prior work, we identified a graph-based model as a promising solution to uniformly manage HPC rich metadata due to its flexibility and generality. However, at the same time, graph-based HPC rich metadata anagement also introducesmore » significant challenges to the underlying infrastructure. In this study, we first identify the challenges on the underlying infrastructure to support scalable, high-performance rich metadata management. Based on that, we introduce GraphMeta, a graphbased engine designed for this use case. It achieves performance scalability by introducing a new graph partitioning algorithm and a write-optimal storage engine. We evaluate GraphMeta under both synthetic and real HPC metadata workloads, compare it with other approaches, and demonstrate its advantages in terms of efficiency and usability for rich metadata management in HPC systems.« less

A graph-based approach to inequality assessment

NASA Astrophysics Data System (ADS)

Palestini, Arsen; Pignataro, Giuseppe

2016-08-01

In a population consisting of heterogeneous types, whose income factors are indicated by nonnegative vectors, policies aggregating different factors can be represented by coalitions in a cooperative game, whose characteristic function is a multi-factor inequality index. When it is not possible to form all coalitions, the feasible ones can be indicated by a graph. We redefine Shapley and Banzhaf values on graph games to deduce some properties involving the degrees of the graph vertices and marginal contributions to overall inequality. An example is finally provided based on a modified multi-factor Atkinson index.

Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.

PubMed

Chen, Lei; Zhang, Yu-Hang; Zheng, Mingyue; Huang, Tao; Cai, Yu-Dong

2016-12-01

Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

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

Zhang, Baichuan; Choudhury, Sutanay; Al-Hasan, Mohammad

2016-02-01

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in termsmore » of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.« less

A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain

PubMed Central

Juarez, Paul D; Hood, Darryl B; Rogers, Gary L; Baktash, Suzanne H; Saxton, Arnold M; Matthews-Juarez, Patricia; Im, Wansoo; Cifuentes, Myriam Patricia; Phillips, Charles A; Lichtveld, Maureen Y; Langston, Michael A

2017-01-01

Objectives The aim is to identify exposures associated with lung cancer mortality and mortality disparities by race and gender using an exposome database coupled to a graph theoretical toolchain. Methods Graph theoretical algorithms were employed to extract paracliques from correlation graphs using associations between 2162 environmental exposures and lung cancer mortality rates in 2067 counties, with clique doubling applied to compute an absolute threshold of significance. Factor analysis and multiple linear regressions then were used to analyze differences in exposures associated with lung cancer mortality and mortality disparities by race and gender. Results While cigarette consumption was highly correlated with rates of lung cancer mortality for both white men and women, previously unidentified novel exposures were more closely associated with lung cancer mortality and mortality disparities for blacks, particularly black women. Conclusions Exposures beyond smoking moderate lung cancer mortality and mortality disparities by race and gender. Policy Implications An exposome approach and database coupled with scalable combinatorial analytics provides a powerful new approach for analyzing relationships between multiple environmental exposures, pathways and health outcomes. An assessment of multiple exposures is needed to appropriately translate research findings into environmental public health practice and policy. PMID:29152601

Graph analysis of functional brain networks: practical issues in translational neuroscience

PubMed Central

De Vico Fallani, Fabrizio; Richiardi, Jonas; Chavez, Mario; Achard, Sophie

2014-01-01

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes. PMID:25180301

Graphs as a Problem-Solving Tool in 1-D Kinematics

ERIC Educational Resources Information Center

Desbien, Dwain M.

2008-01-01

In this age of the microcomputer-based lab (MBL), students are quite accustomed to looking at graphs of position, velocity, and acceleration versus time. A number of textbooks argue convincingly that the slope of the velocity graph gives the acceleration, the area under the velocity graph yields the displacement, and the area under the…

Exact and approximate graph matching using random walks.

PubMed

Gori, Marco; Maggini, Marco; Sarti, Lorenzo

2005-07-01

In this paper, we propose a general framework for graph matching which is suitable for different problems of pattern recognition. The pattern representation we assume is at the same time highly structured, like for classic syntactic and structural approaches, and of subsymbolic nature with real-valued features, like for connectionist and statistic approaches. We show that random walk based models, inspired by Google's PageRank, give rise to a spectral theory that nicely enhances the graph topological features at node level. As a straightforward consequence, we derive a polynomial algorithm for the classic graph isomorphism problem, under the restriction of dealing with Markovian spectrally distinguishable graphs (MSD), a class of graphs that does not seem to be easily reducible to others proposed in the literature. The experimental results that we found on different test-beds of the TC-15 graph database show that the defined MSD class "almost always" covers the database, and that the proposed algorithm is significantly more efficient than top scoring VF algorithm on the same data. Most interestingly, the proposed approach is very well-suited for dealing with partial and approximate graph matching problems, derived for instance from image retrieval tasks. We consider the objects of the COIL-100 visual collection and provide a graph-based representation, whose node's labels contain appropriate visual features. We show that the adoption of classic bipartite graph matching algorithms offers a straightforward generalization of the algorithm given for graph isomorphism and, finally, we report very promising experimental results on the COIL-100 visual collection.

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

PubMed Central

Yu, Qingbao; Erhardt, Erik B.; Sui, Jing; Du, Yuhui; He, Hao; Hjelm, Devon; Cetin, Mustafa S.; Rachakonda, Srinivas; Miller, Robyn L.; Pearlson, Godfrey; Calhoun, Vince D.

2014-01-01

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. PMID:25514514

Mutual proximity graphs for improved reachability in music recommendation.

PubMed

Flexer, Arthur; Stevens, Jeff

2018-01-01

This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists, resulting in poor reachability of the music catalogue. We present mutual proximity graphs, which are an alternative to knn and mutual knn graphs, and are able to avoid hub vertices having abnormally high connectivity. We show that mutual proximity graphs yield much better graph connectivity resulting in improved reachability compared to knn graphs, mutual knn graphs and mutual knn graphs enhanced with minimum spanning trees, while simultaneously reducing the negative effects of hubness.

Mutual proximity graphs for improved reachability in music recommendation

PubMed Central

Flexer, Arthur; Stevens, Jeff

2018-01-01

This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists, resulting in poor reachability of the music catalogue. We present mutual proximity graphs, which are an alternative to knn and mutual knn graphs, and are able to avoid hub vertices having abnormally high connectivity. We show that mutual proximity graphs yield much better graph connectivity resulting in improved reachability compared to knn graphs, mutual knn graphs and mutual knn graphs enhanced with minimum spanning trees, while simultaneously reducing the negative effects of hubness. PMID:29348779

RNA-sequencing-based transcriptome and biochemical analyses of steroidal saponin pathway in a complete set of Allium fistulosum—A. cepa monosomic addition lines

PubMed Central

Abdelrahman, Mostafa; El-Sayed, Magdi; Sato, Shusei; Hirakawa, Hideki; Ito, Shin-ichi; Tanaka, Keisuke; Mine, Yoko; Sugiyama, Nobuo; Suzuki, Minoru; Yamauchi, Naoki

2017-01-01

The genus Allium is a rich source of steroidal saponins, and its medicinal properties have been attributed to these bioactive compounds. The saponin compounds with diverse structures play a pivotal role in Allium’s defense mechanism. Despite numerous studies on the occurrence and chemical structure of steroidal saponins, their biosynthetic pathway in Allium species is poorly understood. The monosomic addition lines (MALs) of the Japanese bunching onion (A. fistulosum, FF) with an extra chromosome from the shallot (A. cepa Aggregatum group, AA) are powerful genetic resources that enable us to understand many physiological traits of Allium. In the present study, we were able to isolate and identify Alliospiroside A saponin compound in A. fistulosum with extra chromosome 2A from shallot (FF2A) and its role in the defense mechanism against Fusarium pathogens. Furthermore, to gain molecular insight into the Allium saponin biosynthesis pathway, high-throughput RNA-Seq of the root, bulb, and leaf of AA, MALs, and FF was carried out using Illumina's HiSeq 2500 platform. An open access Allium Transcript Database (Allium TDB, http://alliumtdb.kazusa.or.jp) was generated based on RNA-Seq data. The resulting assembled transcripts were functionally annotated, revealing 50 unigenes involved in saponin biosynthesis. Differential gene expression (DGE) analyses of AA and MALs as compared with FF (as a control) revealed a strong up-regulation of the saponin downstream pathway, including cytochrome P450, glycosyltransferase, and beta-glucosidase in chromosome 2A. An understanding of the saponin compounds and biosynthesis-related genes would facilitate the development of plants with unique saponin content and, subsequently, improved disease resistance. PMID:28800607

RNA-sequencing-based transcriptome and biochemical analyses of steroidal saponin pathway in a complete set of Allium fistulosum-A. cepa monosomic addition lines.

PubMed

Abdelrahman, Mostafa; El-Sayed, Magdi; Sato, Shusei; Hirakawa, Hideki; Ito, Shin-Ichi; Tanaka, Keisuke; Mine, Yoko; Sugiyama, Nobuo; Suzuki, Yutaka; Yamauchi, Naoki; Shigyo, Masayoshi

2017-01-01

The genus Allium is a rich source of steroidal saponins, and its medicinal properties have been attributed to these bioactive compounds. The saponin compounds with diverse structures play a pivotal role in Allium's defense mechanism. Despite numerous studies on the occurrence and chemical structure of steroidal saponins, their biosynthetic pathway in Allium species is poorly understood. The monosomic addition lines (MALs) of the Japanese bunching onion (A. fistulosum, FF) with an extra chromosome from the shallot (A. cepa Aggregatum group, AA) are powerful genetic resources that enable us to understand many physiological traits of Allium. In the present study, we were able to isolate and identify Alliospiroside A saponin compound in A. fistulosum with extra chromosome 2A from shallot (FF2A) and its role in the defense mechanism against Fusarium pathogens. Furthermore, to gain molecular insight into the Allium saponin biosynthesis pathway, high-throughput RNA-Seq of the root, bulb, and leaf of AA, MALs, and FF was carried out using Illumina's HiSeq 2500 platform. An open access Allium Transcript Database (Allium TDB, http://alliumtdb.kazusa.or.jp) was generated based on RNA-Seq data. The resulting assembled transcripts were functionally annotated, revealing 50 unigenes involved in saponin biosynthesis. Differential gene expression (DGE) analyses of AA and MALs as compared with FF (as a control) revealed a strong up-regulation of the saponin downstream pathway, including cytochrome P450, glycosyltransferase, and beta-glucosidase in chromosome 2A. An understanding of the saponin compounds and biosynthesis-related genes would facilitate the development of plants with unique saponin content and, subsequently, improved disease resistance.

Intuitive color-based visualization of multimedia content as large graphs

NASA Astrophysics Data System (ADS)

Delest, Maylis; Don, Anthony; Benois-Pineau, Jenny

2004-06-01

Data visualization techniques are penetrating in various technological areas. In the field of multimedia such as information search and retrieval in multimedia archives, or digital media production and post-production, data visualization methodologies based on large graphs give an exciting alternative to conventional storyboard visualization. In this paper we develop a new approach to visualization of multimedia (video) documents based both on large graph clustering and preliminary video segmenting and indexing.

[A graph cuts-based interactive method for segmentation of magnetic resonance images of meningioma].

PubMed

Li, Shuan-qiang; Feng, Qian-jin; Chen, Wu-fan; Lin, Ya-zhong

2011-06-01

For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.

Extended Graph-Based Models for Enhanced Similarity Search in Cavbase.

PubMed

Krotzky, Timo; Fober, Thomas; Hüllermeier, Eyke; Klebe, Gerhard

2014-01-01

To calculate similarities between molecular structures, measures based on the maximum common subgraph are frequently applied. For the comparison of protein binding sites, these measures are not fully appropriate since graphs representing binding sites on a detailed atomic level tend to get very large. In combination with an NP-hard problem, a large graph leads to a computationally demanding task. Therefore, for the comparison of binding sites, a less detailed coarse graph model is used building upon so-called pseudocenters. Consistently, a loss of structural data is caused since many atoms are discarded and no information about the shape of the binding site is considered. This is usually resolved by performing subsequent calculations based on additional information. These steps are usually quite expensive, making the whole approach very slow. The main drawback of a graph-based model solely based on pseudocenters, however, is the loss of information about the shape of the protein surface. In this study, we propose a novel and efficient modeling formalism that does not increase the size of the graph model compared to the original approach, but leads to graphs containing considerably more information assigned to the nodes. More specifically, additional descriptors considering surface characteristics are extracted from the local surface and attributed to the pseudocenters stored in Cavbase. These properties are evaluated as additional node labels, which lead to a gain of information and allow for much faster but still very accurate comparisons between different structures.

Granular Flow Graph, Adaptive Rule Generation and Tracking.

PubMed

Pal, Sankar Kumar; Chakraborty, Debarati Bhunia

2017-12-01

A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.

Building occupancy simulation and data assimilation using a graph-based agent-oriented model

NASA Astrophysics Data System (ADS)

Rai, Sanish; Hu, Xiaolin

2018-07-01

Building occupancy simulation and estimation simulates the dynamics of occupants and estimates their real-time spatial distribution in a building. It requires a simulation model and an algorithm for data assimilation that assimilates real-time sensor data into the simulation model. Existing building occupancy simulation models include agent-based models and graph-based models. The agent-based models suffer high computation cost for simulating large numbers of occupants, and graph-based models overlook the heterogeneity and detailed behaviors of individuals. Recognizing the limitations of existing models, this paper presents a new graph-based agent-oriented model which can efficiently simulate large numbers of occupants in various kinds of building structures. To support real-time occupancy dynamics estimation, a data assimilation framework based on Sequential Monte Carlo Methods is also developed and applied to the graph-based agent-oriented model to assimilate real-time sensor data. Experimental results show the effectiveness of the developed model and the data assimilation framework. The major contributions of this work are to provide an efficient model for building occupancy simulation that can accommodate large numbers of occupants and an effective data assimilation framework that can provide real-time estimations of building occupancy from sensor data.

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks.

PubMed

Xu, Zixiang; Sun, Jibin; Wu, Qiaqing; Zhu, Dunming

2017-12-11

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

Library fingerprints: a novel approach to the screening of virtual libraries.

PubMed

Klon, Anthony E; Diller, David J

2007-01-01

We propose a novel method to prioritize libraries for combinatorial synthesis and high-throughput screening that assesses the viability of a particular library on the basis of the aggregate physical-chemical properties of the compounds using a naïve Bayesian classifier. This approach prioritizes collections of related compounds according to the aggregate values of their physical-chemical parameters in contrast to single-compound screening. The method is also shown to be useful in screening existing noncombinatorial libraries when the compounds in these libraries have been previously clustered according to their molecular graphs. We show that the method used here is comparable or superior to the single-compound virtual screening of combinatorial libraries and noncombinatorial libraries and is superior to the pairwise Tanimoto similarity searching of a collection of combinatorial libraries.

A graph-based approach for the retrieval of multi-modality medical images.

PubMed

Kumar, Ashnil; Kim, Jinman; Wen, Lingfeng; Fulham, Michael; Feng, Dagan

2014-02-01

In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging. The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships. We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location. We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects. Copyright © 2013 Elsevier B.V. All rights reserved.

SEQUOIA: significance enhanced network querying through context-sensitive random walk and minimization of network conductance.

PubMed

Jeong, Hyundoo; Yoon, Byung-Jun

2017-03-14

Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results. In this paper, we propose SEQUOIA, a novel network querying algorithm that effectively addresses these issues by utilizing a context-sensitive random walk (CSRW) model for network comparison and minimizing the network conductance of potential matches in the target network. The CSRW model, inspired by the pair hidden Markov model (pair-HMM) that has been widely used for sequence comparison and alignment, can accurately assess the node-to-node correspondence between different graphs by accounting for node insertions and deletions. The proposed algorithm identifies high-scoring network regions based on the CSRW scores, which are subsequently extended by maximally reducing the network conductance of the identified subnetworks. Performance assessment based on real PPI networks and known molecular complexes show that SEQUOIA outperforms existing methods and clearly enhances the biological significance of the query results. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/SEQUOIA .

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

PubMed Central

Ghanbari, Yasser; Smith, Alex R.; Schultz, Robert T.; Verma, Ragini

2014-01-01

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain’s traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. PMID:25037933

Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

PubMed

Hu, Weiming; Gao, Jin; Xing, Junliang; Zhang, Chao; Maybank, Stephen

2017-01-01

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

Measuring Primary Students' Graph Interpretation Skills Via a Performance Assessment: A case study in instrument development

NASA Astrophysics Data System (ADS)

Peterman, Karen; Cranston, Kayla A.; Pryor, Marie; Kermish-Allen, Ruth

2015-11-01

This case study was conducted within the context of a place-based education project that was implemented with primary school students in the USA. The authors and participating teachers created a performance assessment of standards-aligned tasks to examine 6-10-year-old students' graph interpretation skills as part of an exploratory research project. Fifty-five students participated in a performance assessment interview at the beginning and end of a place-based investigation. Two forms of the assessment were created and counterbalanced within class at pre and post. In situ scoring was conducted such that responses were scored as correct versus incorrect during the assessment's administration. Criterion validity analysis demonstrated an age-level progression in student scores. Tests of discriminant validity showed that the instrument detected variability in interpretation skills across each of three graph types (line, bar, dot plot). Convergent validity was established by correlating in situ scores with those from the Graph Interpretation Scoring Rubric. Students' proficiency with interpreting different types of graphs matched expectations based on age and the standards-based progression of graphs across primary school grades. The assessment tasks were also effective at detecting pre-post gains in students' interpretation of line graphs and dot plots after the place-based project. The results of the case study are discussed in relation to the common challenges associated with performance assessment. Implications are presented in relation to the need for authentic and performance-based instructional and assessment tasks to respond to the Common Core State Standards and the Next Generation Science Standards.

Neuro-symbolic representation learning on biological knowledge graphs.

PubMed

Alshahrani, Mona; Khan, Mohammad Asif; Maddouri, Omar; Kinjo, Akira R; Queralt-Rosinach, Núria; Hoehndorf, Robert

2017-09-01

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. https://github.com/bio-ontology-research-group/walking-rdf-and-owl. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Distributed Computation of the knn Graph for Large High-Dimensional Point Sets

PubMed Central

Plaku, Erion; Kavraki, Lydia E.

2009-01-01

High-dimensional problems arising from robot motion planning, biology, data mining, and geographic information systems often require the computation of k nearest neighbor (knn) graphs. The knn graph of a data set is obtained by connecting each point to its k closest points. As the research in the above-mentioned fields progressively addresses problems of unprecedented complexity, the demand for computing knn graphs based on arbitrary distance metrics and large high-dimensional data sets increases, exceeding resources available to a single machine. In this work we efficiently distribute the computation of knn graphs for clusters of processors with message passing. Extensions to our distributed framework include the computation of graphs based on other proximity queries, such as approximate knn or range queries. Our experiments show nearly linear speedup with over one hundred processors and indicate that similar speedup can be obtained with several hundred processors. PMID:19847318

Experimental demonstration of graph-state quantum secret sharing.

PubMed

Bell, B A; Markham, D; Herrera-Martí, D A; Marin, A; Wadsworth, W J; Rarity, J G; Tame, M S

2014-11-21

Quantum communication and computing offer many new opportunities for information processing in a connected world. Networks using quantum resources with tailor-made entanglement structures have been proposed for a variety of tasks, including distributing, sharing and processing information. Recently, a class of states known as graph states has emerged, providing versatile quantum resources for such networking tasks. Here we report an experimental demonstration of graph state-based quantum secret sharing--an important primitive for a quantum network with applications ranging from secure money transfer to multiparty quantum computation. We use an all-optical setup, encoding quantum information into photons representing a five-qubit graph state. We find that one can reliably encode, distribute and share quantum information amongst four parties, with various access structures based on the complex connectivity of the graph. Our results show that graph states are a promising approach for realising sophisticated multi-layered communication protocols in quantum networks.

Technology, Linear Equations, and Buying a Car.

ERIC Educational Resources Information Center

Sandefur, James T.

1992-01-01

Discusses the use of technology in solving compound interest-rate problems that can be modeled by linear relationships. Uses a graphing calculator to solve the specific problem of determining the amount of money that can be borrowed to buy a car for a given monthly payment and interest rate. (MDH)

An "EAR" on environmental surveillance and monitoring: A ...

EPA Pesticide Factsheets

Current environmental monitoring approaches focus primarily on chemical occurrence. However, based on chemical concentration alone, it can be difficult to identify which compounds may be of toxicological concern for prioritization for further monitoring or management. This can be problematic because toxicological characterization is lacking for many emerging contaminants. New sources of high throughput screening data like the ToxCast™ database, which contains data for over 9,000 compounds screened through up to 1,100 assays, are now available. Integrated analysis of chemical occurrence data with HTS data offers new opportunities to prioritize chemicals, sites, or biological effects for further investigation based on concentrations detected in the environment linked to relative potencies in pathway-based bioassays. As a case study, chemical occurrence data from a 2012 study in the Great Lakes Basin along with the ToxCast™ effects database were used to calculate exposure-activity ratios (EARs) as a prioritization tool. Technical considerations of data processing and use of the ToxCast™ database are presented and discussed. EAR prioritization identified multiple sites, biological pathways, and chemicals that warrant further investigation. Biological pathways were then linked to adverse outcome pathways to identify potential adverse outcomes and biomarkers for use in subsequent monitoring efforts. Anthropogenic contaminants are frequently reported in environm

The investigation of social networks based on multi-component random graphs

NASA Astrophysics Data System (ADS)

Zadorozhnyi, V. N.; Yudin, E. B.

2018-01-01

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.

A clustering-based graph Laplacian framework for value function approximation in reinforcement learning.

PubMed

Xu, Xin; Huang, Zhenhua; Graves, Daniel; Pedrycz, Witold

2014-12-01

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

Development of genetically engineered bacteria for production of selected aromatic compounds

DOEpatents

Ward, Thomas E.; Watkins, Carolyn S.; Bulmer, Deborah K.; Johnson, Bruce F.; Amaratunga, Mohan

2001-01-01

The cloning and expression of genes in the common aromatic pathway of E. coli are described. A compound for which chorismate, the final product of the common aromatic pathway, is an anabolic intermediate can be produced by cloning and expressing selected genes of the common aromatic pathway and the genes coding for enzymes necessary to convert chorismate to the selected compound. Plasmids carrying selected genes of the common aromatic pathway are also described.

A Graph Based Backtracking Algorithm for Solving General CSPs

NASA Technical Reports Server (NTRS)

Pang, Wanlin; Goodwin, Scott D.

2003-01-01

Many AI tasks can be formalized as constraint satisfaction problems (CSPs), which involve finding values for variables subject to constraints. While solving a CSP is an NP-complete task in general, tractable classes of CSPs have been identified based on the structure of the underlying constraint graphs. Much effort has been spent on exploiting structural properties of the constraint graph to improve the efficiency of finding a solution. These efforts contributed to development of a class of CSP solving algorithms called decomposition algorithms. The strength of CSP decomposition is that its worst-case complexity depends on the structural properties of the constraint graph and is usually better than the worst-case complexity of search methods. Its practical application is limited, however, since it cannot be applied if the CSP is not decomposable. In this paper, we propose a graph based backtracking algorithm called omega-CDBT, which shares merits and overcomes the weaknesses of both decomposition and search approaches.

Predicting the points of interaction of small molecules in the NF-κB pathway

PubMed Central

2011-01-01

Background The similarity property principle has been used extensively in drug discovery to identify small compounds that interact with specific drug targets. Here we show it can be applied to identify the interactions of small molecules within the NF-κB signalling pathway. Results Clusters that contain compounds with a predominant interaction within the pathway were created, which were then used to predict the interaction of compounds not included in the clustering analysis. Conclusions The technique successfully predicted the points of interactions of compounds that are known to interact with the NF-κB pathway. The method was also shown to be successful when compounds for which the interaction points were unknown were included in the clustering analysis. PMID:21342508

Robust Algorithms for on Minor-Free Graphs Based on the Sherali-Adams Hierarchy

NASA Astrophysics Data System (ADS)

Magen, Avner; Moharrami, Mohammad

This work provides a Linear Programming-based Polynomial Time Approximation Scheme (PTAS) for two classical NP-hard problems on graphs when the input graph is guaranteed to be planar, or more generally Minor Free. The algorithm applies a sufficiently large number (some function of when approximation is required) of rounds of the so-called Sherali-Adams Lift-and-Project system. needed to obtain a -approximation, where f is some function that depends only on the graph that should be avoided as a minor. The problem we discuss are the well-studied problems, the and problems. An curious fact we expose is that in the world of minor-free graph, the is harder in some sense than the.

A unified framework for building high performance DVEs

NASA Astrophysics Data System (ADS)

Lei, Kaibin; Ma, Zhixia; Xiong, Hua

2011-10-01

A unified framework for integrating PC cluster based parallel rendering with distributed virtual environments (DVEs) is presented in this paper. While various scene graphs have been proposed in DVEs, it is difficult to enable collaboration of different scene graphs. This paper proposes a technique for non-distributed scene graphs with the capability of object and event distribution. With the increase of graphics data, DVEs require more powerful rendering ability. But general scene graphs are inefficient in parallel rendering. The paper also proposes a technique to connect a DVE and a PC cluster based parallel rendering environment. A distributed multi-player video game is developed to show the interaction of different scene graphs and the parallel rendering performance on a large tiled display wall.

A sediment graph model based on SCS-CN method

NASA Astrophysics Data System (ADS)

Singh, P. K.; Bhunya, P. K.; Mishra, S. K.; Chaube, U. C.

2008-01-01

SummaryThis paper proposes new conceptual sediment graph models based on coupling of popular and extensively used methods, viz., Nash model based instantaneous unit sediment graph (IUSG), soil conservation service curve number (SCS-CN) method, and Power law. These models vary in their complexity and this paper tests their performance using data of the Nagwan watershed (area = 92.46 km 2) (India). The sensitivity of total sediment yield and peak sediment flow rate computations to model parameterisation is analysed. The exponent of the Power law, β, is more sensitive than other model parameters. The models are found to have substantial potential for computing sediment graphs (temporal sediment flow rate distribution) as well as total sediment yield.

Semantic Drift in Espresso-style Bootstrapping: Graph-theoretic Analysis and Evaluation in Word Sense Disambiguation

NASA Astrophysics Data System (ADS)

Komachi, Mamoru; Kudo, Taku; Shimbo, Masashi; Matsumoto, Yuji

Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of Espresso-style bootstrapping has the same root as the topic drift of Kleinberg's HITS, using a simplified graph-based reformulation of bootstrapping. We confirm that two graph-based algorithms, the von Neumann kernels and the regularized Laplacian, can reduce the effect of semantic drift in the task of word sense disambiguation (WSD) on Senseval-3 English Lexical Sample Task. Proposed algorithms achieve superior performance to Espresso and previous graph-based WSD methods, even though the proposed algorithms have less parameters and are easy to calibrate.

Compound C induces protective autophagy in human cholangiocarcinoma cells via Akt/mTOR-independent pathway.

PubMed

Zhao, Xiaofang; Luo, Guosong; Cheng, Ying; Yu, Wenjing; Chen, Run; Xiao, Bin; Xiang, Yuancai; Feng, Chunhong; Fu, Wenguang; Duan, Chunyan; Yao, Fuli; Xia, Xianming; Tao, Qinghua; Wei, Mei; Dai, Rongyang

2018-07-01

Compound C, a well-known inhibitor of AMP-activated protein kinase (AMPK), has been reported to exert antitumor activities in some types of cells. Whether compound C can exert antitumor effects in human cholangiocarcinoma (CCA) remains unknown. Here, we demonstrated that compound C is a potent inducer of cell death and autophagy in human CCA cells. Autophagy inhibitors increased the cytotoxicity of compound C towards human CCA cells, as confirmed by increased LDH release, and PARP cleavage. It is notable that compound C treatment increased phosphorylated Akt, sustained high levels of phosphorylated p70S6K, and decreased mTOR regulated p-ULK1 (ser757). Based on the data that blocking PI3K/Akt or mTOR had no apparent influence on autophagic response, we suggest that compound C induces autophagy independent of Akt/mTOR signaling in human CCA cells. Further study demonstrated that compound C inhibited the phosphorylation of JNK and its target c-Jun. Blocking JNK by SP600125 or siRNA suppressed autophagy induction upon compound C treatment. Moreover, compound C induced p38 MAPK activation, and its inhibition promoted autophagy induction via JNK activation. In addition, compound C induced p53 expression, and its inhibition attenuated compound C-induced autophagic response. Thus, compound C triggers autophagy, at least in part, via the JNK and p53 pathways in human CCA cells. In conclusion, suppresses autophagy could increase compound C sensitivity in human CCA. © 2018 Wiley Periodicals, Inc.

Brain Graph Topology Changes Associated with Anti-Epileptic Drug Use

PubMed Central

Levin, Harvey S.; Chiang, Sharon

2015-01-01

Abstract Neuroimaging studies of functional connectivity using graph theory have furthered our understanding of the network structure in temporal lobe epilepsy (TLE). Brain network effects of anti-epileptic drugs could influence such studies, but have not been systematically studied. Resting-state functional MRI was analyzed in 25 patients with TLE using graph theory analysis. Patients were divided into two groups based on anti-epileptic medication use: those taking carbamazepine/oxcarbazepine (CBZ/OXC) (n=9) and those not taking CBZ/OXC (n=16) as a part of their medication regimen. The following graph topology metrics were analyzed: global efficiency, betweenness centrality (BC), clustering coefficient, and small-world index. Multiple linear regression was used to examine the association of CBZ/OXC with graph topology. The two groups did not differ from each other based on epilepsy characteristics. Use of CBZ/OXC was associated with a lower BC. Longer epilepsy duration was also associated with a lower BC. These findings can inform graph theory-based studies in patients with TLE. The changes observed are discussed in relation to the anti-epileptic mechanism of action and adverse effects of CBZ/OXC. PMID:25492633

Graphs and Tracks Revisited

NASA Astrophysics Data System (ADS)

Christian, Wolfgang; Belloni, Mario

2013-04-01

We have recently developed a Graphs and Tracks model based on an earlier program by David Trowbridge, as shown in Fig. 1. Our model can show position, velocity, acceleration, and energy graphs and can be used for motion-to-graphs exercises. Users set the heights of the track segments, and the model displays the motion of the ball on the track together with position, velocity, and acceleration graphs. This ready-to-run model is available in the ComPADRE OSP Collection at www.compadre.org/osp/items/detail.cfm?ID=12023.

Program for Generating Graphs and Charts

NASA Technical Reports Server (NTRS)

Ackerson, C. T.

1986-01-01

Office Automation Pilot (OAP) Graphics Database system offers IBM personal computer user assistance in producing wide variety of graphs and charts and convenient data-base system, called chart base, for creating and maintaining data associated with graphs and charts. Thirteen different graphics packages available. Access graphics capabilities obtained in similar manner. User chooses creation, revision, or chartbase-maintenance options from initial menu; Enters or modifies data displayed on graphic chart. OAP graphics data-base system written in Microsoft PASCAL.

VitaPad: visualization tools for the analysis of pathway data.

PubMed

Holford, Matthew; Li, Naixin; Nadkarni, Prakash; Zhao, Hongyu

2005-04-15

Packages that support the creation of pathway diagrams are limited by their inability to be readily extended to new classes of pathway-related data. VitaPad is a cross-platform application that enables users to create and modify biological pathway diagrams and incorporate microarray data with them. It improves on existing software in the following areas: (i) It can create diagrams dynamically through graph layout algorithms. (ii) It is open-source and uses an open XML format to store data, allowing for easy extension or integration with other tools. (iii) It features a cutting-edge user interface with intuitive controls, high-resolution graphics and fully customizable appearance. http://bioinformatics.med.yale.edu matthew.holford@yale.edu; hongyu.zhao@yale.edu.

Inferring molecular interactions pathways from eQTL data

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

Rashid, Imran; McDermott, Jason E.; Samudrala, Ram

Analysis of expression quantitative trait loci (eQTL) helps elucidate the connection between genotype, gene expression levels, and phenotype. However, standard statistical genetics can only attribute changes in expression levels to loci on the genome, not specific genes. Each locus can contain many genes, making it very difficult to discover which gene is controlling the expression levels of other genes. Furthermore, it is even more difficult to find a pathway of molecular interactions responsible for controlling the expression levels. Here we describe a series of techniques for finding explanatory pathways by exploring graphs of molecular interactions. We show several simple methodsmore » can find complete pathways the explain the mechanism of differential expression in eQTL data.« less

Production of Odd-Carbon Dicarboxylic Acids in Escherichia coli Using an Engineered Biotin-Fatty Acid Biosynthetic Pathway.

PubMed

Haushalter, Robert W; Phelan, Ryan M; Hoh, Kristina M; Su, Cindy; Wang, George; Baidoo, Edward E K; Keasling, Jay D

2017-04-05

Dicarboxylic acids are commodity chemicals used in the production of plastics, polyesters, nylons, fragrances, and medications. Bio-based routes to dicarboxylic acids are gaining attention due to environmental concerns about petroleum-based production of these compounds. Some industrial applications require dicarboxylic acids with specific carbon chain lengths, including odd-carbon species. Biosynthetic pathways involving cytochrome P450-catalyzed oxidation of fatty acids in yeast and bacteria have been reported, but these systems produce almost exclusively even-carbon species. Here we report a novel pathway to odd-carbon dicarboxylic acids directly from glucose in Escherichia coli by employing an engineered pathway combining enzymes from biotin and fatty acid synthesis. Optimization of the pathway will lead to industrial strains for the production of valuable odd-carbon diacids.

Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets

PubMed Central

Kotera, Masaaki; Tabei, Yasuo; Yamanishi, Yoshihiro; Tokimatsu, Toshiaki; Goto, Susumu

2013-01-01

Motivation: The metabolic pathway is an important biochemical reaction network involving enzymatic reactions among chemical compounds. However, it is assumed that a large number of metabolic pathways remain unknown, and many reactions are still missing even in known pathways. Therefore, the most important challenge in metabolomics is the automated de novo reconstruction of metabolic pathways, which includes the elucidation of previously unknown reactions to bridge the metabolic gaps. Results: In this article, we develop a novel method to reconstruct metabolic pathways from a large compound set in the reaction-filling framework. We define feature vectors representing the chemical transformation patterns of compound–compound pairs in enzymatic reactions using chemical fingerprints. We apply a sparsity-induced classifier to learn what we refer to as ‘enzymatic-reaction likeness’, i.e. whether compound pairs are possibly converted to each other by enzymatic reactions. The originality of our method lies in the search for potential reactions among many compounds at a time, in the extraction of reaction-related chemical transformation patterns and in the large-scale applicability owing to the computational efficiency. In the results, we demonstrate the usefulness of our proposed method on the de novo reconstruction of 134 metabolic pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG). Our comprehensively predicted reaction networks of 15 698 compounds enable us to suggest many potential pathways and to increase research productivity in metabolomics. Availability: Softwares are available on request. Supplementary material are available at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2013/. Contact: goto@kuicr.kyoto-u.ac.jp PMID:23812977

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

PubMed

Bakal, Gokhan; Talari, Preetham; Kakani, Elijah V; Kavuluru, Ramakanth

2018-06-01

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction. Copyright © 2018 Elsevier Inc. All rights reserved.

A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration

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

Wong, Pak C.; Haglin, David J.; Gillen, David S.

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with ~one trillion edges and ~50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with ~25 billion edgesmore » in less than two hours and 2) support database query and visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.« less

Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

PubMed Central

Gómez-Adorno, Helena; Sidorov, Grigori; Pinto, David; Vilariño, Darnes; Gelbukh, Alexander

2016-01-01

We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution. PMID:27589740

A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds

NASA Astrophysics Data System (ADS)

Xiong, B.; Oude Elberink, S.; Vosselman, G.

2014-07-01

In the task of 3D building model reconstruction from point clouds we face the problem of recovering a roof topology graph in the presence of noise, small roof faces and low point densities. Errors in roof topology graphs will seriously affect the final modelling results. The aim of this research is to automatically correct these errors. We define the graph correction as a graph-to-graph problem, similar to the spelling correction problem (also called the string-to-string problem). The graph correction is more complex than string correction, as the graphs are 2D while strings are only 1D. We design a strategy based on a dictionary of graph edit operations to automatically identify and correct the errors in the input graph. For each type of error the graph edit dictionary stores a representative erroneous subgraph as well as the corrected version. As an erroneous roof topology graph may contain several errors, a heuristic search is applied to find the optimum sequence of graph edits to correct the errors one by one. The graph edit dictionary can be expanded to include entries needed to cope with errors that were previously not encountered. Experiments show that the dictionary with only fifteen entries already properly corrects one quarter of erroneous graphs in about 4500 buildings, and even half of the erroneous graphs in one test area, achieving as high as a 95% acceptance rate of the reconstructed models.

On the Certain Topological Indices of Titania Nanotube TiO2[m, n

NASA Astrophysics Data System (ADS)

Javaid, M.; Liu, Jia-Bao; Rehman, M. A.; Wang, Shaohui

2017-07-01

A numeric quantity that characterises the whole structure of a molecular graph is called the topological index that predicts the physical features, chemical reactivities, and boiling activities of the involved chemical compound in the molecular graph. In this article, we give new mathematical expressions for the multiple Zagreb indices, the generalised Zagreb index, the fourth version of atom-bond connectivity (ABC4) index, and the fifth version of geometric-arithmetic (GA5) index of TiO2[m, n]. In addition, we compute the latest developed topological index called by Sanskruti index. At the end, a comparison is also included to estimate the efficiency of the computed indices. Our results extended some known conclusions.

Concordant Chemical Reaction Networks and the Species-Reaction Graph

PubMed Central

Shinar, Guy; Feinberg, Martin

2015-01-01

In a recent paper it was shown that, for chemical reaction networks possessing a subtle structural property called concordance, dynamical behavior of a very circumscribed (and largely stable) kind is enforced, so long as the kinetics lies within the very broad and natural weakly monotonic class. In particular, multiple equilibria are precluded, as are degenerate positive equilibria. Moreover, under certain circumstances, also related to concordance, all real eigenvalues associated with a positive equilibrium are negative. Although concordance of a reaction network can be decided by readily available computational means, we show here that, when a nondegenerate network’s Species-Reaction Graph satisfies certain mild conditions, concordance and its dynamical consequences are ensured. These conditions are weaker than earlier ones invoked to establish kinetic system injectivity, which, in turn, is just one ramification of network concordance. Because the Species-Reaction Graph resembles pathway depictions often drawn by biochemists, results here expand the possibility of inferring significant dynamical information directly from standard biochemical reaction diagrams. PMID:22940368

A technology mapping based on graph of excitations and outputs for finite state machines

NASA Astrophysics Data System (ADS)

Kania, Dariusz; Kulisz, Józef

2017-11-01

A new, efficient technology mapping method of FSMs, dedicated for PAL-based PLDs is proposed. The essence of the method consists in searching for the minimal set of PAL-based logic blocks that cover a set of multiple-output implicants describing the transition and output functions of an FSM. The method is based on a new concept of graph: the Graph of Excitations and Outputs. The proposed algorithm was tested using the FSM benchmarks. The obtained results were compared with the classical technology mapping of FSM.

Overlapping community detection based on link graph using distance dynamics

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Jing; Cai, Li-Jun

2018-01-01

The distance dynamics model was recently proposed to detect the disjoint community of a complex network. To identify the overlapping structure of a network using the distance dynamics model, an overlapping community detection algorithm, called L-Attractor, is proposed in this paper. The process of L-Attractor mainly consists of three phases. In the first phase, L-Attractor transforms the original graph to a link graph (a new edge graph) to assure that one node has multiple distances. In the second phase, using the improved distance dynamics model, a dynamic interaction process is introduced to simulate the distance dynamics (shrink or stretch). Through the dynamic interaction process, all distances converge, and the disjoint community structure of the link graph naturally manifests itself. In the third phase, a recovery method is designed to convert the disjoint community structure of the link graph to the overlapping community structure of the original graph. Extensive experiments are conducted on the LFR benchmark networks as well as real-world networks. Based on the results, our algorithm demonstrates higher accuracy and quality than other state-of-the-art algorithms.

Large-scale DCMs for resting-state fMRI.

PubMed

Razi, Adeel; Seghier, Mohamed L; Zhou, Yuan; McColgan, Peter; Zeidman, Peter; Park, Hae-Jeong; Sporns, Olaf; Rees, Geraint; Friston, Karl J

2017-01-01

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity . This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM-with functional connectivity priors-is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

Discovery of a novel and potent class of F. tularensis enoyl-reductase (FabI) inhibitors by molecular shape and electrostatic matching

PubMed Central

Hevener, Kirk E.; Mehboob, Shahila; Su, Pin-Chih; Truong, Kent; Boci, Teuta; Deng, Jiangping; Ghassemi, Mahmood; Cook, James L.; Johnson, Michael E.

2011-01-01

Enoyl-acyl carrier protein (ACP) reductase, FabI, is a key enzyme in the bacterial fatty acid biosynthesis pathway (FAS II). FabI is an NADH-dependent oxidoreductase that acts to reduce enoyl-ACP substrates in a final step of the pathway. The absence of this enzyme in humans makes it an attractive target for the development of new antibacterial agents. FabI is known to be unresponsive to structure-based design efforts due to a high degree of induced fit and a mobile flexible loop encompassing the active site. Here we discuss the development, validation, and careful application of a ligand-based virtual screen used for the identification of novel inhibitors of the Francisella tularensis FabI target. In this study, four known classes of FabI inhibitors were used as templates for virtual screens that involved molecular shape and electrostatic matching. The program ROCS was used to search a high-throughput screening library for compounds that matched any of the four molecular shape queries. Matching compounds were further refined using the program EON, which compares and scores compounds by matching electrostatic properties. Using these techniques, 50 compounds were selected, ordered, and tested. The tested compounds possessed novel chemical scaffolds when compared to the input query compounds. Several hits with low micromolar activity were identified and follow-up scaffold-based searches resulted in the identification of a lead series with sub-micromolar enzyme inhibition, high ligand efficiency, and a novel scaffold. Additionally, one of the most active compounds showed promising whole-cell antibacterial activity against several Gram-positive and Gram-negative species, including the target pathogen. The results of a preliminary structure-activity relationship analysis are presented. PMID:22098466

Embryotoxic and pharmacologic potency ranking of six azoles in the rat whole embryo culture by morphological and transcriptomic analysis

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

Dimopoulou, Myrto, E-mail: myrto.dimopoulou@wur.nl

Differential gene expression analysis in the rat whole embryo culture (WEC) assay provides mechanistic insight into the embryotoxicity of test compounds. In our study, we hypothesized that comparative analysis of the transcriptomes of rat embryos exposed to six azoles (flusilazole, triadimefon, ketoconazole, miconazole, difenoconazole and prothioconazole) could lead to a better mechanism-based understanding of their embryotoxicity and pharmacological action. For evaluating embryotoxicity, we applied the total morphological scoring system (TMS) in embryos exposed for 48 h. The compounds tested showed embryotoxicity in a dose-response fashion. Functional analysis of differential gene expression after 4 h exposure at the ID{sub 10} (effectivemore » dose for 10% decreased TMS), revealed the sterol biosynthesis pathway and embryonic development genes, dominated by genes in the retinoic acid (RA) pathway, albeit in a differential way. Flusilazole, ketoconazole and triadimefon were the most potent compounds affecting the RA pathway, while in terms of regulation of sterol function, difenoconazole and ketoconazole showed the most pronounced effects. Dose-dependent analysis of the effects of flusilazole revealed that the RA pathway related genes were already differentially expressed at low dose levels while the sterol pathway showed strong regulation at higher embryotoxic doses, suggesting that this pathway is less predictive for the observed embryotoxicity. A similar analysis at the 24-hour time point indicated an additional time-dependent difference in the aforementioned pathways regulated by flusilazole. In summary, the rat WEC assay in combination with transcriptomics could add a mechanistic insight into the embryotoxic potency ranking and pharmacological mode of action of the tested compounds. - Highlights: • Embryonic exposure to azoles revealed concentration-dependent malformations. • Transcriptomics could enhance the mechanistic knowledge of embryotoxicants. • Retinoic acid gene set identifies early embryotoxic responses to azoles. • Toxic versus pharmacologic potency determines functional efficacy.« less

New thymine-based derivative of nitrogen mustards.

PubMed

Boëns, Benjamin; Teste, Karine; Hadj-Bouazza, Amel; Ismaili, Jihane; Zerrouki, Rachida

2012-01-01

This work deals with the synthesis of a new nitrogen mustard derivative based on thymine. To introduce the bis(2-chloroethyl)amine group to position 4 of the pyrimidine base, many strategies were explored and the desired compound was finally obtained, thanks to a synthetic pathway in five steps.

Mapping the genetic and tissular diversity of 64 phenolic compounds in Citrus species using a UPLC–MS approach

PubMed Central

Durand-Hulak, Marie; Dugrand, Audray; Duval, Thibault; Bidel, Luc P. R.; Jay-Allemand, Christian; Froelicher, Yann; Bourgaud, Frédéric; Fanciullino, Anne-Laure

2015-01-01

Background and Aims Phenolic compounds contribute to food quality and have potential health benefits. Consequently, they are an important target of selection for Citrus species. Numerous studies on this subject have revealed new molecules, potential biosynthetic pathways and linkage between species. Although polyphenol profiles are correlated with gene expression, which is responsive to developmental and environmental cues, these factors are not monitored in most studies. A better understanding of the biosynthetic pathway and its regulation requires more information about environmental conditions, tissue specificity and connections between competing sub-pathways. This study proposes a rapid method, from sampling to analysis, that allows the quantitation of multiclass phenolic compounds across contrasting tissues and cultivars. Methods Leaves and fruits of 11 cultivated citrus of commercial interest were collected from adult trees grown in an experimental orchard. Sixty-four phenolic compounds were simultaneously quantified by ultra-high-performance liquid chromatography coupled with mass spectrometry. Key Results Combining data from vegetative tissues with data from fruit tissues improved cultivar classification based on polyphenols. The analysis of metabolite distribution highlighted the massive accumulation of specific phenolic compounds in leaves and the external part of the fruit pericarp, which reflects their involvement in plant defence. The overview of the biosynthetic pathway obtained confirmed some regulatory steps, for example those catalysed by rhamnosyltransferases. The results suggest that three other steps are responsible for the different metabolite profiles in ‘Clementine’ and ‘Star Ruby’ grapefruit. Conclusions The method described provides a high-throughput method to study the distribution of phenolic compounds across contrasting tissues and cultivars in Citrus, and offers the opportunity to investigate their regulation and physiological roles. The method was validated in four different tissues and allowed the identification and quantitation of 64 phenolic compounds in 20 min, which represents an improvement over existing methods of analysing multiclass polyphenols. PMID:25757470

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering.

PubMed

Peng, Xi; Yu, Zhiding; Yi, Zhang; Tang, Huajin

2017-04-01

Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that l 1 -, l 2 -, l ∞ -, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.

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.

Anisotropic Laplace-Beltrami Eigenmaps: Bridging Reeb Graphs and Skeletons*

PubMed Central

Shi, Yonggang; Lai, Rongjie; Krishna, Sheila; Sicotte, Nancy; Dinov, Ivo; Toga, Arthur W.

2010-01-01

In this paper we propose a novel approach of computing skeletons of robust topology for simply connected surfaces with boundary by constructing Reeb graphs from the eigenfunctions of an anisotropic Laplace-Beltrami operator. Our work brings together the idea of Reeb graphs and skeletons by incorporating a flux-based weight function into the Laplace-Beltrami operator. Based on the intrinsic geometry of the surface, the resulting Reeb graph is pose independent and captures the global profile of surface geometry. Our algorithm is very efficient and it only takes several seconds to compute on neuroanatomical structures such as the cingulate gyrus and corpus callosum. In our experiments, we show that the Reeb graphs serve well as an approximate skeleton with consistent topology while following the main body of conventional skeletons quite accurately. PMID:21339850

Dim target detection method based on salient graph fusion

NASA Astrophysics Data System (ADS)

Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

2018-02-01

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure.

PubMed

Chen, Langdong; Cao, Yan; Zhang, Hai; Lv, Diya; Zhao, Yahong; Liu, Yanjun; Ye, Guan; Chai, Yifeng

2018-01-31

Yangxinshi tablet (YXST) is an effective treatment for heart failure and myocardial infarction; it consists of 13 herbal medicines formulated according to traditional Chinese Medicine (TCM) practices. It has been used for the treatment of cardiovascular disease for many years in China. In this study, a network pharmacology-based strategy was used to elucidate the mechanism of action of YXST for the treatment of heart failure. Cardiovascular disease-related protein target and compound databases were constructed for YXST. A molecular docking platform was used to predict the protein targets of YXST. The affinity between proteins and ingredients was determined using surface plasmon resonance (SPR) assays. The action modes between targets and representative ingredients were calculated using Glide docking, and the related pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A protein target database containing 924 proteins was constructed; 179 compounds in YXST were identified, and 48 compounds with high relevance to the proteins were defined as representative ingredients. Thirty-four protein targets of the 48 representative ingredients were analyzed and classified into two categories: immune and cardiovascular systems. The SPR assay and molecular docking partly validated the interplay between protein targets and representative ingredients. Moreover, 28 pathways related to heart failure were identified, which provided directions for further research on YXST. This study demonstrated that the cardiovascular protective effect of YXST mainly involved the immune and cardiovascular systems. Through the research strategy based on network pharmacology, we analysis the complex system of YXST and found 48 representative compounds, 34 proteins and 28 related pathways of YXST, which could help us understand the underlying mechanism of YSXT's anti-heart failure effect. The network-based investigation could help researchers simplify the complex system of YXSY. It may also offer a feasible approach to decipher the chemical and pharmacological bases of other TCM formulas. Copyright © 2018 Elsevier B.V. All rights reserved.

Multi-Level Anomaly Detection on Time-Varying Graph Data

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

Bridges, Robert A; Collins, John P; Ferragut, Erik M

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, thismore » multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.« less

Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines

PubMed Central

Zhang, Kai; Lan, Liang; Kwok, James T.; Vucetic, Slobodan; Parvin, Bahram

2014-01-01

When the amount of labeled data are limited, semi-supervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via ℓ1-regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning. PMID:25720002

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

PubMed Central

Doktorova, T. Y.; Yildirimman, Reha; Ceelen, Liesbeth; Vilardell, Mireia; Vanhaecke, Tamara; Vinken, Mathieu; Ates, Gamze; Heymans, Anja; Gmuender, Hans; Bort, Roque; Corvi, Raffaella; Phrakonkham, Pascal; Li, Ruoya; Mouchet, Nicolas; Chesne, Christophe; van Delft, Joost; Kleinjans, Jos; Castell, Jose; Herwig, Ralf; Rogiers, Vera

2014-01-01

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88 % correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances. PMID:26417288

Computationally efficient characterization of potential energy surfaces based on fingerprint distances

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

Schaefer, Bastian; Goedecker, Stefan, E-mail: stefan.goedecker@unibas.ch

2016-07-21

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows us to understand important characteristics like thermodynamic, dynamic, and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This methodmore » allows us to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows us to generate an approximate network of the minima, their connectivity, and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling.« less

Lung vessel segmentation in CT images using graph-cuts

NASA Astrophysics Data System (ADS)

Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

2016-03-01

Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

Multi-stage tandem mass spectrometric analysis of novel β-cyclodextrin-substituted and novel bis-pyridinium gemini surfactants designed as nanomedical drug delivery agents.

PubMed

Donkuru, McDonald; Chitanda, Jackson M; Verrall, Ronald E; El-Aneed, Anas

2014-04-15

This study aimed at evaluating the collision-induced dissociation tandem mass spectrometric (CID-MS/MS) fragmentation patterns of novel β-cyclodextrin-substituted- and bis-pyridinium gemini surfactants currently being explored as nanomaterial drug delivery agents. In the β-cyclodextrin-substituted gemini surfactants, a β-cyclodextrin ring is grafted onto an N,N-bis(dimethylalkyl)-α,ω-aminoalkane-diammonium moiety using variable succinyl linkers. In contrast, the bis-pyridinium gemini surfactants are based on a 1,1'-(1,1'-(ethane-1,2-diylbis(sulfanediyl))bis(alkane-2,1-diyl))dipyridinium template, defined by two symmetrical N-alkylpyridinium parts connected through a fixed ethane dithiol spacer. Detection of the precursor ion [M](2+) species of the synthesized compounds and the determination of mass accuracies were conducted using a QqTOF-MS instrument. A multi-stage tandem MS analysis of the detected [M](2+) species was conducted using the QqQ-LIT-MS instrument. Both instruments were equipped with an electrospray ionization (ESI) source. Abundant precursor ion [M](2+) species were detected for all compounds at sub-1 ppm mass accuracies. The β-cyclodextrin-substituted compounds, fragmented via two main pathways: Pathway 1: the loss of one head-tail region produces a [M-(N(Me)2-R)](2+) ion, from which sugar moieties (Glc) are sequentially cleaved; Pathway 2: both head-tail regions are lost to give [M-2(N(Me)2-R)](+), followed by consecutive loss of Glc units. Alternatively, the cleavage of the Glc units could also have occurred simultaneously. Nevertheless, the fragmentation evolved around the quaternary ammonium cations, with characteristic cleavage of Glc moieties. For the bis-pyridinium gemini compounds, they either lost neutral pyridine(s) to give doubly charged ions (Pathway A) or formed complementary pyridinium alongside other singly charged ions (Pathway B). Similar to β-cyclodextrin-substituted compounds, the fragmentation was centered on the pyridinium functional groups. The MS(n) analyses of these novel gemini surfactants, reported here for the first time, revealed diagnostic ions for each compound, with a universal fragmentation pattern for each compound series. The diagnostic ions will be employed within liquid chromatography (LC)/MS/MS methods for screening, identification, and quantification of these compounds within biological samples. Copyright © 2014 John Wiley & Sons, Ltd.

On construction method of shipborne and airborne radar intelligence and related equipment knowledge graph

NASA Astrophysics Data System (ADS)

Hao, Ruizhe; Huang, Jian

2017-08-01

Knowledge graph construction in military intelligence domain is sprouting but technically immature. This paper presents a method to construct the heterogeneous knowledge graph in the field of shipborne and airborne radar and equipment. Based on the expert knowledge and the up-to-date Internet open source information, we construct the knowledge graph of radar characteristic information and the equipment respectively, and establish relationships between two graphs, providing the pipeline and method for the intelligence organization and management in the context of the crowding battlefields big data.

A Graph-Centric Approach for Metagenome-Guided Peptide and Protein Identification in Metaproteomics

PubMed Central

Tang, Haixu; Li, Sujun; Ye, Yuzhen

2016-01-01

Metaproteomic studies adopt the common bottom-up proteomics approach to investigate the protein composition and the dynamics of protein expression in microbial communities. When matched metagenomic and/or metatranscriptomic data of the microbial communities are available, metaproteomic data analyses often employ a metagenome-guided approach, in which complete or fragmental protein-coding genes are first directly predicted from metagenomic (and/or metatranscriptomic) sequences or from their assemblies, and the resulting protein sequences are then used as the reference database for peptide/protein identification from MS/MS spectra. This approach is often limited because protein coding genes predicted from metagenomes are incomplete and fragmental. In this paper, we present a graph-centric approach to improving metagenome-guided peptide and protein identification in metaproteomics. Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded in the community. We tested our method using several public metaproteomic datasets with matched metagenomic and metatranscriptomic sequencing data acquired from complex microbial communities in a biological wastewater treatment plant. The results showed that many more peptides and proteins can be identified when assembly graphs were utilized, improving the characterization of the proteins expressed in the microbial communities. The additional proteins we identified contribute to the characterization of important pathways such as those involved in degradation of chemical hazards. Our tools are released as open-source software on github at https://github.com/COL-IU/Graph2Pro. PMID:27918579

Biosynthetic Pathway for the Epipolythiodioxopiperazine Acetylaranotin in Aspergillus terreus Revealed by Genome-based Deletion Analysis

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

Guo, Chun-Jun; Yeh, Hsu-Hua; Chiang, Yi Ming

2013-04-15

Abstract Epipolythiodioxopiperazines (ETPs) are a class of fungal secondary metabolites derived from cyclic peptides. Acetylaranotin belongs to one structural subgroup of ETPs characterized by the presence of a seven-membered dihydrooxepine ring. Defining the genes involved in acetylaranotin biosynthesis should provide a means to increase production of these compounds and facilitate the engineering of second-generation molecules. The filamentous fungus Aspergillus terreus produces acetylaranotin and related natural products. Using targeted gene deletions, we have identified a cluster of 9 genes including one nonribosomal peptide synthase gene, ataP, that is required for acetylaranotin biosynthesis. Chemical analysis of the wild type and mutant strainsmore » enabled us to isolate seventeen natural products that are either intermediates in the normal biosynthetic pathway or shunt products that are produced when the pathway is interrupted through mutation. Nine of the compounds identified in this study are novel natural products. Our data allow us to propose a complete biosynthetic pathway for acetylaranotin and related natural products.« less

QSPR modeling: graph connectivity indices versus line graph connectivity indices

PubMed

Basak; Nikolic; Trinajstic; Amic; Beslo

2000-07-01

Five QSPR models of alkanes were reinvestigated. Properties considered were molecular surface-dependent properties (boiling points and gas chromatographic retention indices) and molecular volume-dependent properties (molar volumes and molar refractions). The vertex- and edge-connectivity indices were used as structural parameters. In each studied case we computed connectivity indices of alkane trees and alkane line graphs and searched for the optimum exponent. Models based on indices with an optimum exponent and on the standard value of the exponent were compared. Thus, for each property we generated six QSPR models (four for alkane trees and two for the corresponding line graphs). In all studied cases QSPR models based on connectivity indices with optimum exponents have better statistical characteristics than the models based on connectivity indices with the standard value of the exponent. The comparison between models based on vertex- and edge-connectivity indices gave in two cases (molar volumes and molar refractions) better models based on edge-connectivity indices and in three cases (boiling points for octanes and nonanes and gas chromatographic retention indices) better models based on vertex-connectivity indices. Thus, it appears that the edge-connectivity index is more appropriate to be used in the structure-molecular volume properties modeling and the vertex-connectivity index in the structure-molecular surface properties modeling. The use of line graphs did not improve the predictive power of the connectivity indices. Only in one case (boiling points of nonanes) a better model was obtained with the use of line graphs.

Multiclass Data Segmentation Using Diffuse Interface Methods on Graphs

DTIC Science & Technology

2014-01-01

interac- tive image segmentation using the solution to a combinatorial Dirichlet problem. Elmoataz et al . have developed general- izations of the graph...Laplacian [25] for image denoising and manifold smoothing. Couprie et al . in [18] define a conve- niently parameterized graph-based energy function that...over to the discrete graph representation. For general data segmentation, Bresson et al . in [8], present rigorous convergence results for two algorithms

SensiPath: computer-aided design of sensing-enabling metabolic pathways.

PubMed

Delépine, Baudoin; Libis, Vincent; Carbonell, Pablo; Faulon, Jean-Loup

2016-07-08

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Human EST Ontology Explorer: a tissue-oriented visualization system for ontologies distribution in human EST collections.

PubMed

Merelli, Ivan; Caprera, Andrea; Stella, Alessandra; Del Corvo, Marcello; Milanesi, Luciano; Lazzari, Barbara

2009-10-15

The NCBI dbEST currently contains more than eight million human Expressed Sequenced Tags (ESTs). This wide collection represents an important source of information for gene expression studies, provided it can be inspected according to biologically relevant criteria. EST data can be browsed using different dedicated web resources, which allow to investigate library specific gene expression levels and to make comparisons among libraries, highlighting significant differences in gene expression. Nonetheless, no tool is available to examine distributions of quantitative EST collections in Gene Ontology (GO) categories, nor to retrieve information concerning library-dependent EST involvement in metabolic pathways. In this work we present the Human EST Ontology Explorer (HEOE) http://www.itb.cnr.it/ptp/human_est_explorer, a web facility for comparison of expression levels among libraries from several healthy and diseased tissues. The HEOE provides library-dependent statistics on the distribution of sequences in the GO Direct Acyclic Graph (DAG) that can be browsed at each GO hierarchical level. The tool is based on large-scale BLAST annotation of EST sequences. Due to the huge number of input sequences, this BLAST analysis was performed with the aid of grid computing technology, which is particularly suitable to address data parallel task. Relying on the achieved annotation, library-specific distributions of ESTs in the GO Graph were inferred. A pathway-based search interface was also implemented, for a quick evaluation of the representation of libraries in metabolic pathways. EST processing steps were integrated in a semi-automatic procedure that relies on Perl scripts and stores results in a MySQL database. A PHP-based web interface offers the possibility to simultaneously visualize, retrieve and compare data from the different libraries. Statistically significant differences in GO categories among user selected libraries can also be computed. The HEOE provides an alternative and complementary way to inspect EST expression levels with respect to approaches currently offered by other resources. Furthermore, BLAST computation on the whole human EST dataset was a suitable test of grid scalability in the context of large-scale bioinformatics analysis. The HEOE currently comprises sequence analysis from 70 non-normalized libraries, representing a comprehensive overview on healthy and unhealthy tissues. As the analysis procedure can be easily applied to other libraries, the number of represented tissues is intended to increase.

Multiple graph regularized protein domain ranking.

PubMed

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2012-11-19

Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

Multiple graph regularized protein domain ranking

PubMed Central

2012-01-01

Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. PMID:23157331

A Graph Summarization Algorithm Based on RFID Logistics

NASA Astrophysics Data System (ADS)

Sun, Yan; Hu, Kongfa; Lu, Zhipeng; Zhao, Li; Chen, Ling

Radio Frequency Identification (RFID) applications are set to play an essential role in object tracking and supply chain management systems. The volume of data generated by a typical RFID application will be enormous as each item will generate a complete history of all the individual locations that it occupied at every point in time. The movement trails of such RFID data form gigantic commodity flowgraph representing the locations and durations of the path stages traversed by each item. In this paper, we use graph to construct a warehouse of RFID commodity flows, and introduce a database-style operation to summarize graphs, which produces a summary graph by grouping nodes based on user-selected node attributes, further allows users to control the hierarchy of summaries. It can cut down the size of graphs, and provide convenience for users to study just on the shrunk graph which they interested. Through extensive experiments, we demonstrate the effectiveness and efficiency of the proposed method.

M-Polynomials and topological indices of V-Phenylenic Nanotubes and Nanotori.

PubMed

Kwun, Young Chel; Munir, Mobeen; Nazeer, Waqas; Rafique, Shazia; Min Kang, Shin

2017-08-18

V-Phenylenic nanotubes and nanotori are most comprehensively studied nanostructures due to widespread applications in the production of catalytic, gas-sensing and corrosion-resistant materials. Representing chemical compounds with M-polynomial is a recent idea and it produces nice formulas of degree-based topological indices which correlate chemical properties of the material under investigation. These indices are used in the development of quantitative structure-activity relationships (QSARs) in which the biological activity and other properties of molecules like boiling point, stability, strain energy etc. are correlated with their structures. In this paper, we determine general closed formulae for M-polynomials of V-Phylenic nanotubes and nanotori. We recover important topological degree-based indices. We also give different graphs of topological indices and their relations with the parameters of structures.

Graph drawing using tabu search coupled with path relinking.

PubMed

Dib, Fadi K; Rodgers, Peter

2018-01-01

Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search based methods for graph drawing which are based on optimizing an objective function which is formed from a weighted sum of multiple criteria. In this paper, we propose a new neighbourhood search method which uses a tabu search coupled with path relinking to optimize such objective functions for general graph layouts with undirected straight lines. To our knowledge, before our work, neither of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimization techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (objective function's value) and the speed of layout in terms of the number of evaluated solutions required to draw a graph. We also examine the relative scalability of each method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can layout larger graphs than the state-of-the-art neighbourhood search methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset.

Graph drawing using tabu search coupled with path relinking

PubMed Central

Rodgers, Peter

2018-01-01

Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search based methods for graph drawing which are based on optimizing an objective function which is formed from a weighted sum of multiple criteria. In this paper, we propose a new neighbourhood search method which uses a tabu search coupled with path relinking to optimize such objective functions for general graph layouts with undirected straight lines. To our knowledge, before our work, neither of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimization techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (objective function’s value) and the speed of layout in terms of the number of evaluated solutions required to draw a graph. We also examine the relative scalability of each method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can layout larger graphs than the state-of-the-art neighbourhood search methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset. PMID:29746576

Subtype and pathway specific responses to anticancer compounds in breast cancer.

PubMed

Heiser, Laura M; Sadanandam, Anguraj; Kuo, Wen-Lin; Benz, Stephen C; Goldstein, Theodore C; Ng, Sam; Gibb, William J; Wang, Nicholas J; Ziyad, Safiyyah; Tong, Frances; Bayani, Nora; Hu, Zhi; Billig, Jessica I; Dueregger, Andrea; Lewis, Sophia; Jakkula, Lakshmi; Korkola, James E; Durinck, Steffen; Pepin, François; Guan, Yinghui; Purdom, Elizabeth; Neuvial, Pierre; Bengtsson, Henrik; Wood, Kenneth W; Smith, Peter G; Vassilev, Lyubomir T; Hennessy, Bryan T; Greshock, Joel; Bachman, Kurtis E; Hardwicke, Mary Ann; Park, John W; Marton, Laurence J; Wolf, Denise M; Collisson, Eric A; Neve, Richard M; Mills, Gordon B; Speed, Terence P; Feiler, Heidi S; Wooster, Richard F; Haussler, David; Stuart, Joshua M; Gray, Joe W; Spellman, Paul T

2012-02-21

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation.

PubMed

Song, Jingkuan; Gao, Lianli; Nie, Feiping; Shen, Heng Tao; Yan, Yan; Sebe, Nicu

2016-11-01

In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometry-based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimized graph (OGL) from multi-cues (i.e., partial tags and multiple features), which can more accurately embed the relationships among the data points. Since OGL is a transductive method and cannot deal with novel data points, we further extend our model to address the out-of-sample issue. Extensive experiments on image and video annotation show the consistent superiority of OGL over the state-of-the-art methods.

Weights and topology: a study of the effects of graph construction on 3D image segmentation.

PubMed

Grady, Leo; Jolly, Marie-Pierre

2008-01-01

Graph-based algorithms have become increasingly popular for medical image segmentation. The fundamental process for each of these algorithms is to use the image content to generate a set of weights for the graph and then set conditions for an optimal partition of the graph with respect to these weights. To date, the heuristics used for generating the weighted graphs from image intensities have largely been ignored, while the primary focus of attention has been on the details of providing the partitioning conditions. In this paper we empirically study the effects of graph connectivity and weighting function on the quality of the segmentation results. To control for algorithm-specific effects, we employ both the Graph Cuts and Random Walker algorithms in our experiments.

Fast generation of sparse random kernel graphs

DOE PAGES

Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

2015-09-10

The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

An automatic graph-based approach for artery/vein classification in retinal images.

PubMed

Dashtbozorg, Behdad; Mendonça, Ana Maria; Campilho, Aurélio

2014-03-01

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

A Set of Handwriting Features for Use in Automated Writer Identification.

PubMed

Miller, John J; Patterson, Robert Bradley; Gantz, Donald T; Saunders, Christopher P; Walch, Mark A; Buscaglia, JoAnn

2017-05-01

A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system described in this article has been implemented in a highly accurate and approximately language-independent biometric recognition system of writers of cursive documents. © 2017 American Academy of Forensic Sciences.

Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

PubMed

Arch-Int, Ngamnij; Arch-Int, Somjit; Sonsilphong, Suphachoke; Wanchai, Paweena

2017-01-01

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.

Graph-Based Semantic Web Service Composition for Healthcare Data Integration

PubMed Central

2017-01-01

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement. PMID:29065602

Production of Odd-Carbon Dicarboxylic Acids in Escherichia coli Using an Engineered Biotin–Fatty Acid Biosynthetic Pathway

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

Haushalter, Robert W.; Phelan, Ryan M.; Hoh, Kristina M.

Dicarboxylic acids are commodity chemicals used in the production of plastics, polyesters, nylons, fragrances, and medications. Bio-based routes to dicarboxylic acids are gaining attention due to environmental concerns about petroleum-based production of these compounds. Some industrial applications require dicarboxylic acids with specific carbon chain lengths, including odd-carbon species. Biosynthetic pathways involving cytochrome P450-catalyzed oxidation of fatty acids in yeast and bacteria have been reported, but these systems produce almost exclusively even-carbon species. Here in this paper we report a novel pathway to odd-carbon dicarboxylic acids directly from glucose in Escherichia coli by employing an engineered pathway combining enzymes from biotinmore » and fatty acid synthesis. Optimization of the pathway will lead to industrial strains for the production of valuable odd-carbon diacids.« less

A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph

PubMed Central

Pan, Yongke; Niu, Wenjia

2017-01-01

Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines. PMID:28316616

Co-occurrence graphs for word sense disambiguation in the biomedical domain.

PubMed

Duque, Andres; Stevenson, Mark; Martinez-Romo, Juan; Araujo, Lourdes

2018-05-01

Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. In this paper, we present a new graph-based unsupervised technique to address this problem. The knowledge base used in this work is a graph built with co-occurrence information from medical concepts found in scientific abstracts, and hence adapted to the specific domain. Unlike other unsupervised approaches based on static graphs such as UMLS, in this work the knowledge base takes the context of the ambiguous terms into account. Abstracts downloaded from PubMed are used for building the graph and disambiguation is performed using the personalized PageRank algorithm. Evaluation is carried out over two test datasets widely explored in the literature. Different parameters of the system are also evaluated to test robustness and scalability. Results show that the system is able to outperform state-of-the-art knowledge-based systems, obtaining more than 10% of accuracy improvement in some cases, while only requiring minimal external resources. Copyright © 2018 Elsevier B.V. All rights reserved.

Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study

PubMed Central

Yu, Qingbao; Wu, Lei; Bridwell, David A.; Erhardt, Erik B.; Du, Yuhui; He, Hao; Chen, Jiayu; Liu, Peng; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D.

2016-01-01

The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics. PMID:27733821

Recovering metabolic pathways via optimization.

PubMed

Beasley, John E; Planes, Francisco J

2007-01-01

A metabolic pathway is a coherent set of enzyme catalysed biochemical reactions by which a living organism transforms an initial (source) compound into a final (target) compound. Some of the different metabolic pathways adopted within organisms have been experimentally determined. In this paper, we show that a number of experimentally determined metabolic pathways can be recovered by a mathematical optimization model.

Computing Information Value from RDF Graph Properties

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

al-Saffar, Sinan; Heileman, Gregory

2010-11-08

Information value has been implicitly utilized and mostly non-subjectively computed in information retrieval (IR) systems. We explicitly define and compute the value of an information piece as a function of two parameters, the first is the potential semantic impact the target information can subjectively have on its recipient's world-knowledge, and the second parameter is trust in the information source. We model these two parameters as properties of RDF graphs. Two graphs are constructed, a target graph representing the semantics of the target body of information and a context graph representing the context of the consumer of that information. We computemore » information value subjectively as a function of both potential change to the context graph (impact) and the overlap between the two graphs (trust). Graph change is computed as a graph edit distance measuring the dissimilarity between the context graph before and after the learning of the target graph. A particular application of this subjective information valuation is in the construction of a personalized ranking component in Web search engines. Based on our method, we construct a Web re-ranking system that personalizes the information experience for the information-consumer.« less

A Networks Approach to Modeling Enzymatic Reactions.

PubMed

Imhof, P

2016-01-01

Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.

BMDExpress Data Viewer: A Visualization Tool to Analyze ...

EPA Pesticide Factsheets

Regulatory agencies increasingly apply benchmark dose (BMD) modeling to determine points of departure in human risk assessments. BMDExpress applies BMD modeling to transcriptomics datasets and groups genes to biological processes and pathways for rapid assessment of doses at which biological perturbations occur. However, graphing and analytical capabilities within BMDExpress are limited, and the analysis of output files is challenging. We developed a web-based application, BMDExpress Data Viewer, for visualization and graphical analyses of BMDExpress output files. The software application consists of two main components: ‘Summary Visualization Tools’ and ‘Dataset Exploratory Tools’. We demonstrate through two case studies that the ‘Summary Visualization Tools’ can be used to examine and assess the distributions of probe and pathway BMD outputs, as well as derive a potential regulatory BMD through the modes or means of the distributions. The ‘Functional Enrichment Analysis’ tool presents biological processes in a two-dimensional bubble chart view. By applying filters of pathway enrichment p-value and minimum number of significant genes, we showed that the Functional Enrichment Analysis tool can be applied to select pathways that are potentially sensitive to chemical perturbations. The ‘Multiple Dataset Comparison’ tool enables comparison of BMDs across multiple experiments (e.g., across time points, tissues, or organisms, etc.). The ‘BMDL-BM

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

Hierarchical graphs for better annotations of rule-based models of biochemical systems

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

Hu, Bin; Hlavacek, William

2009-01-01

In the graph-based formalism of the BioNetGen language (BNGL), graphs are used to represent molecules, with a colored vertex representing a component of a molecule, a vertex label representing the internal state of a component, and an edge representing a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions, with a rule that specifies addition (removal) of an edge representing a class of association (dissociation) reactions and with a rule that specifies a change of vertex label representing a class of reactions that affect the internal state of amore » molecular component. A set of rules comprises a mathematical/computational model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Here, for purposes of model annotation, we propose an extension of BNGL that involves the use of hierarchical graphs to represent (1) relationships among components and subcomponents of molecules and (2) relationships among classes of reactions defined by rules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR)/CD3 complex. Likewise, we illustrate how hierarchical graphs can be used to document the similarity of two related rules for kinase-catalyzed phosphorylation of a protein substrate. We also demonstrate how a hierarchical graph representing a protein can be encoded in an XML-based format.« less

Synthesis of some novel orcinol based coumarin triazole hybrids with capabilities to inhibit RANKL-induced osteoclastogenesis through NF-κB signaling pathway.

PubMed

Rama Krishna, Boddu; Thummuri, Dinesh; Naidu, V G M; Ramakrishna, Sistla; Venkata Mallavadhani, Uppuluri

2018-08-01

A total of twenty-two novel coumarin triazole hybrids (4a-4k and 6a-6k) were synthesized from orcinol in good to excellent yields of 70-94%. The structures of all the synthesized compounds were elucidated by spectroscopic techniques such as 1 H NMR, 13 C NMR, and HRMS. The anti-inflammatory potential of synthesized compounds was investigated against the proinflammatory cytokine, TNF-α on U937 cell line and compounds 4d, 4j, and 6j were found to exhibit promising anti-inflammatory activity. These three compounds were further screened against TNF-α on LPS-stimulated RAW 264.7 cells, which confirm their anti-inflammatory potential. Furthermore, the above said active compounds were tested for their inhibitory effect on RANKL-induced osteoclastogenesis in RAW 264.7 cells by using tartrate resistant acid phosphatase (TRAP) staining assay at 10 µM. Molecular mechanism studies demonstrated that compound 4d exhibited dose dependent inhibition of RANKL-induced osteoclastogenesis by suppression of the NF-kB pathway. Thus, compound 4d is a promising candidate for further optimization to develop as a potent anti-osteoporotic agent. Copyright © 2018 Elsevier Inc. All rights reserved.

Application-Specific Graph Sampling for Frequent Subgraph Mining and Community Detection

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

Purohit, Sumit; Choudhury, Sutanay; Holder, Lawrence B.

Graph mining is an important data analysis methodology, but struggles as the input graph size increases. The scalability and usability challenges posed by such large graphs make it imperative to sample the input graph and reduce its size. The critical challenge in sampling is to identify the appropriate algorithm to insure the resulting analysis does not suffer heavily from the data reduction. Predicting the expected performance degradation for a given graph and sampling algorithm is also useful. In this paper, we present different sampling approaches for graph mining applications such as Frequent Subgrpah Mining (FSM), and Community Detection (CD). Wemore » explore graph metrics such as PageRank, Triangles, and Diversity to sample a graph and conclude that for heterogeneous graphs Triangles and Diversity perform better than degree based metrics. We also present two new sampling variations for targeted graph mining applications. We present empirical results to show that knowledge of the target application, along with input graph properties can be used to select the best sampling algorithm. We also conclude that performance degradation is an abrupt, rather than gradual phenomena, as the sample size decreases. We present the empirical results to show that the performance degradation follows a logistic function.« less

Graph characterization via Ihara coefficients.

PubMed

Ren, Peng; Wilson, Richard C; Hancock, Edwin R

2011-02-01

The novel contributions of this paper are twofold. First, we demonstrate how to characterize unweighted graphs in a permutation-invariant manner using the polynomial coefficients from the Ihara zeta function, i.e., the Ihara coefficients. Second, we generalize the definition of the Ihara coefficients to edge-weighted graphs. For an unweighted graph, the Ihara zeta function is the reciprocal of a quasi characteristic polynomial of the adjacency matrix of the associated oriented line graph. Since the Ihara zeta function has poles that give rise to infinities, the most convenient numerically stable representation is to work with the coefficients of the quasi characteristic polynomial. Moreover, the polynomial coefficients are invariant to vertex order permutations and also convey information concerning the cycle structure of the graph. To generalize the representation to edge-weighted graphs, we make use of the reduced Bartholdi zeta function. We prove that the computation of the Ihara coefficients for unweighted graphs is a special case of our proposed method for unit edge weights. We also present a spectral analysis of the Ihara coefficients and indicate their advantages over other graph spectral methods. We apply the proposed graph characterization method to capturing graph-class structure and clustering graphs. Experimental results reveal that the Ihara coefficients are more effective than methods based on Laplacian spectra.

What Would a Graph Look Like in this Layout? A Machine Learning Approach to Large Graph Visualization.

PubMed

Kwon, Oh-Hyun; Crnovrsanin, Tarik; Ma, Kwan-Liu

2018-01-01

Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a "good" layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.

Learning a Health Knowledge Graph from Electronic Medical Records.

PubMed

Rotmensch, Maya; Halpern, Yoni; Tlimat, Abdulhakim; Horng, Steven; Sontag, David

2017-07-20

Demand for clinical decision support systems in medicine and self-diagnostic symptom checkers has substantially increased in recent years. Existing platforms rely on knowledge bases manually compiled through a labor-intensive process or automatically derived using simple pairwise statistics. This study explored an automated process to learn high quality knowledge bases linking diseases and symptoms directly from electronic medical records. Medical concepts were extracted from 273,174 de-identified patient records and maximum likelihood estimation of three probabilistic models was used to automatically construct knowledge graphs: logistic regression, naive Bayes classifier and a Bayesian network using noisy OR gates. A graph of disease-symptom relationships was elicited from the learned parameters and the constructed knowledge graphs were evaluated and validated, with permission, against Google's manually-constructed knowledge graph and against expert physician opinions. Our study shows that direct and automated construction of high quality health knowledge graphs from medical records using rudimentary concept extraction is feasible. The noisy OR model produces a high quality knowledge graph reaching precision of 0.85 for a recall of 0.6 in the clinical evaluation. Noisy OR significantly outperforms all tested models across evaluation frameworks (p < 0.01).

PANDA: pathway and annotation explorer for visualizing and interpreting gene-centric data.

PubMed

Hart, Steven N; Moore, Raymond M; Zimmermann, Michael T; Oliver, Gavin R; Egan, Jan B; Bryce, Alan H; Kocher, Jean-Pierre A

2015-01-01

Objective. Bringing together genomics, transcriptomics, proteomics, and other -omics technologies is an important step towards developing highly personalized medicine. However, instrumentation has advances far beyond expectations and now we are able to generate data faster than it can be interpreted. Materials and Methods. We have developed PANDA (Pathway AND Annotation) Explorer, a visualization tool that integrates gene-level annotation in the context of biological pathways to help interpret complex data from disparate sources. PANDA is a web-based application that displays data in the context of well-studied pathways like KEGG, BioCarta, and PharmGKB. PANDA represents data/annotations as icons in the graph while maintaining the other data elements (i.e., other columns for the table of annotations). Custom pathways from underrepresented diseases can be imported when existing data sources are inadequate. PANDA also allows sharing annotations among collaborators. Results. In our first use case, we show how easy it is to view supplemental data from a manuscript in the context of a user's own data. Another use-case is provided describing how PANDA was leveraged to design a treatment strategy from the somatic variants found in the tumor of a patient with metastatic sarcomatoid renal cell carcinoma. Conclusion. PANDA facilitates the interpretation of gene-centric annotations by visually integrating this information with context of biological pathways. The application can be downloaded or used directly from our website: http://bioinformaticstools.mayo.edu/research/panda-viewer/.

Physiologically-based pharmacokinetic (PBPK) modeling to explore potential metabolic pathways of bromochloromethane in rats.

EPA Science Inventory

Bromochloromethane (BCM) is a volatile organic compound and a by-product of disinfection of water by chlorination. Physiologically based pharmacokinetic (PBPK) models are used in risk assessment applications and a PBPK model for BCM, Updated with F-344 specific input parameters,...

Physiologically-based pharmacokinetic (PBPK) modeling to explore potential metabolic pathways of bromochloromethane in rats

EPA Science Inventory

Bromochloromethane (BCM) is a volatile compound and a by-product of disinfection of water by ofchlorination. Physiologically based pharmacokinetic (PBPK) models are used in risk assessment applications. An updated PBPKmodel for BCM is generated and applied to hypotheses testing c...

High-throughput bioluminescence screening of ubiquitin-proteasome pathway inhibitors from chemical and natural sources.

PubMed

Ausseil, Frederic; Samson, Arnaud; Aussagues, Yannick; Vandenberghe, Isabelle; Creancier, Laurent; Pouny, Isabelle; Kruczynski, Anna; Massiot, Georges; Bailly, Christian

2007-02-01

To discover original inhibitors of the ubiquitin-proteasome pathway, the authors have developed a cell-based bioluminescent assay and used it to screen collections of plant extracts and chemical compounds. They first established a DLD-1 human colon cancer cell line that stably expresses a 4Ubiquitin-Luciferase (4Ub-Luc) reporter protein, efficiently targeted to the ubiquitin-proteasome degradation pathway. The assay was then adapted to 96- and 384-well plate formats and calibrated with reference proteasome inhibitors. Assay robustness was carefully assessed, particularly cell toxicity, and the statistical Z factor value was calculated to 0.83, demonstrating a good performance level of the assay. A total of 18,239 molecules and 15,744 plant extracts and fractions thereof were screened for their capacity to increase the luciferase activity in DLD-1 4Ub-Luc cells, and 21 molecules and 66 extracts inhibiting the ubiquitin-proteasome pathway were identified. The fractionation of an active methanol extract of Physalis angulata L. aerial parts was performed to isolate 2 secosteroids known as physalin B and C. In a cell-based Western blot assay, the ubiquitinated protein accumulation was confirmed after a physalin treatment confirming the accuracy of the screening process. The method reported here thus provides a robust approach to identify novel ubiquitin-proteasome pathway inhibitors in large collections of chemical compounds and natural products.

deBGR: an efficient and near-exact representation of the weighted de Bruijn graph

PubMed Central

Pandey, Prashant; Bender, Michael A.; Johnson, Rob; Patro, Rob

2017-01-01

Abstract Motivation: Almost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using ‘long read’ technologies like those offered by PacBio or Oxford Nanopore), efficient k-mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k-mer occurs, which is key in transcriptome assemblers. Results: We present a method for compactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially no errors. Our representation yields zero errors while increasing the space requirements by less than 18–28% compared to the approximate de Bruijn graph representation in Squeakr. Our technique is based on a simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of general interest and applicable in most weighted de Bruijn Graph-based systems. Availability and implementation: https://github.com/splatlab/debgr. Contact: rob.patro@cs.stonybrook.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881995

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

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

Bridges, Robert A.; Collins, John P.; Ferragut, Erik M.

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitatesmore » intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. Furthermore, to illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.« less

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

DOE PAGES

Bridges, Robert A.; Collins, John P.; Ferragut, Erik M.; ...

2016-01-01

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitatesmore » intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. Furthermore, to illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.« less

Study of amyloid-β peptide functional brain networks in AD, MCI and HC.

PubMed

Jiang, Jiehui; Duan, Huoqiang; Huang, Zheming; Yu, Zhihua

2015-01-01

One medical challenge in studying the amyloid-β (Aβ) peptide mechanism for Alzheimer's disease (AD) is exploring the law of beta toxic oligomers' diffusion in human brains in vivo. One beneficial means of solving this problem is brain network analysis based on graph theory. In this study, the characteristics of Aβ functional brain networks of Healthy Control (HC), Mild Cognitive Impairment (MCI), and AD groups were compared by applying graph theoretical analyses to Carbon 11-labeled Pittsburgh compound B positron emission tomography (11C PiB-PET) data. 120 groups of PiB-PET images from the ADNI database were analyzed. The results showed that the small-world property of MCI and AD were lost as compared to HC. Furthermore, the local clustering of networks was higher in both MCI and AD as compared to HC, whereas the path length was similar among the three groups. The results also showed that there could be four potential Aβ toxic oligomer seeds: Frontal_Sup_Medial_L, Parietal_Inf_L, Frontal_Med_Orb_R, and Parietal_Inf_R. These four seeds are corresponding to Regions of Interests referred by physicians to clinically diagnose AD.

TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data.

PubMed

Huang, Xiaoke; Zhao, Ye; Yang, Jing; Zhang, Chong; Ma, Chao; Ye, Xinyue

2016-01-01

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.

Diagnostic and Remedial Learning Strategy Based on Conceptual Graphs

ERIC Educational Resources Information Center

Jong, BinShyan; Lin, TsongWuu; Wu, YuLung; Chan, Teyi

2004-01-01

Numerous scholars have applied conceptual graphs for explanatory purposes. This study devised the Remedial-Instruction Decisive path (RID path) algorithm for diagnosing individual student learning situation. This study focuses on conceptual graphs. According to the concepts learned by students and the weight values of relations among these…

Reactions Involved in the Lower Pathway for Degradation of 4-Nitrotoluene by Mycobacterium Strain HL 4-NT-1

PubMed Central

He, Zhongqi; Spain, Jim C.

2000-01-01

In spite of the variety of initial reactions, the aerobic biodegradation of aromatic compounds generally yields dihydroxy intermediates for ring cleavage. Recent investigation of the degradation of nitroaromatic compounds revealed that some nitroaromatic compounds are initially converted to 2-aminophenol rather than dihydroxy intermediates by a number of microorganisms. The complete pathway for the metabolism of 2-aminophenol during the degradation of nitrobenzene by Pseudomonas pseudoalcaligenes JS45 has been elucidated previously. The pathway is parallel to the catechol extradiol ring cleavage pathway, except that 2-aminophenol is the ring cleavage substrate. Here we report the elucidation of the pathway of 2-amino-4-methylphenol (6-amino-m-cresol) metabolism during the degradation of 4-nitrotoluene by Mycobacterium strain HL 4-NT-1 and the comparison of the substrate specificities of the relevant enzymes in strains JS45 and HL 4-NT-1. The results indicate that the 2-aminophenol ring cleavage pathway in strain JS45 is not unique but is representative of the pathways of metabolism of other o-aminophenolic compounds. PMID:10877799

Graph-state formalism for mutually unbiased bases

NASA Astrophysics Data System (ADS)

Spengler, Christoph; Kraus, Barbara

2013-11-01

A pair of orthonormal bases is called mutually unbiased if all mutual overlaps between any element of one basis and an arbitrary element of the other basis coincide. In case the dimension, d, of the considered Hilbert space is a power of a prime number, complete sets of d+1 mutually unbiased bases (MUBs) exist. Here we present a method based on the graph-state formalism to construct such sets of MUBs. We show that for n p-level systems, with p being prime, one particular graph suffices to easily construct a set of pn+1 MUBs. In fact, we show that a single n-dimensional vector, which is associated with this graph, can be used to generate a complete set of MUBs and demonstrate that this vector can be easily determined. Finally, we discuss some advantages of our formalism regarding the analysis of entanglement structures in MUBs, as well as experimental realizations.

Identification of TRAIL-inducing compounds highlights small molecule ONC201/TIC10 as a unique anti-cancer agent that activates the TRAIL pathway.

PubMed

Allen, Joshua E; Krigsfeld, Gabriel; Patel, Luv; Mayes, Patrick A; Dicker, David T; Wu, Gen Sheng; El-Deiry, Wafik S

2015-05-01

We previously reported the identification of ONC201/TIC10, a novel small molecule inducer of the human TRAIL gene that improves efficacy-limiting properties of recombinant TRAIL and is in clinical trials in advanced cancers based on its promising safety and antitumor efficacy in several preclinical models. We performed a high throughput luciferase reporter screen using the NCI Diversity Set II to identify TRAIL-inducing compounds. Small molecule-mediated induction of TRAIL reporter activity was relatively modest and the majority of the hit compounds induced low levels of TRAIL upregulation. Among the candidate TRAIL-inducing compounds, TIC9 and ONC201/TIC10 induced sustained TRAIL upregulation and apoptosis in tumor cells in vitro and in vivo. However, ONC201/TIC10 potentiated tumor cell death while sparing normal cells, unlike TIC9, and lacked genotoxicity in normal fibroblasts. Investigating the effects of TRAIL-inducing compounds on cell signaling pathways revealed that TIC9 and ONC201/TIC10, which are the most potent inducers of cell death, exclusively activate Foxo3a through inactivation of Akt/ERK to upregulate TRAIL and its pro-apoptotic death receptor DR5. These studies reveal the selective activity of ONC201/TIC10 that led to its selection as a lead compound for this novel class of antitumor agents and suggest that ONC201/TIC10 is a unique inducer of the TRAIL pathway through its concomitant regulation of the TRAIL ligand and its death receptor DR5.

Identification and Metabolite Profiling of Chemical Activators of Lipid Accumulation in Green Algae1[OPEN

PubMed Central

2017-01-01

Microalgae are proposed as feedstock organisms useful for producing biofuels and coproducts. However, several limitations must be overcome before algae-based production is economically feasible. Among these is the ability to induce lipid accumulation and storage without affecting biomass yield. To overcome this barrier, a chemical genetics approach was employed in which 43,783 compounds were screened against Chlamydomonas reinhardtii, and 243 compounds were identified that increase triacylglyceride (TAG) accumulation without terminating growth. Identified compounds were classified by structural similarity, and 15 were selected for secondary analyses addressing impacts on growth fitness, photosynthetic pigments, and total cellular protein and starch concentrations. TAG accumulation was verified using gas chromatography-mass spectrometry quantification of total fatty acids, and targeted TAG and galactolipid measurements were performed using liquid chromatography-multiple reaction monitoring/mass spectrometry. These results demonstrated that TAG accumulation does not necessarily proceed at the expense of galactolipid. Untargeted metabolite profiling provided important insights into pathway shifts due to five different compound treatments and verified the anabolic state of the cells with regard to the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways. Metabolite patterns were distinct from nitrogen starvation and other abiotic stresses commonly used to induce oil accumulation in algae. The efficacy of these compounds also was demonstrated in three other algal species. These lipid-inducing compounds offer a valuable set of tools for delving into the biochemical mechanisms of lipid accumulation in algae and a direct means to improve algal oil content independent of the severe growth limitations associated with nutrient deprivation. PMID:28652262

A metabolomics-based approach identifies changes in the small molecular weight compound composition of the peanut as a result of dry-roasting.

PubMed

Klevorn, Claire M; Dean, Lisa L

2018-02-01

Raw peanuts in the USA are subjected to thermal processing, such as dry-roasting, prior to consumption. A multi-instrument metabolomics-based platform along with targeted analyses was used to determine changes in the low-molecular-weight compound composition of peanuts due to dry-roasting. Runner and virginia-type peanut seeds were characterized using several analytical platforms including (RP)/UPLC-MS/MS (positive and negative ion mode ESI) and HILIC/UPLC-MS/MS with negative ion mode ESI. Of the 383 compounds identified, 16 compounds were unique to the roasted peanuts. Using pathway analysis, compounds associated with arginine and proline metabolism were found to be the most changed. Products of chemical degradation and compounds contained within the vesicular bodies of the peanut increased after roasting. Dry-roasting had a significant impact on the levels and types of low-molecular-weight compounds present. These findings provide useful information about composition changes due to roasting. Published by Elsevier Ltd.

Graph-based linear scaling electronic structure theory.

PubMed

Niklasson, Anders M N; Mniszewski, Susan M; Negre, Christian F A; Cawkwell, Marc J; Swart, Pieter J; Mohd-Yusof, Jamal; Germann, Timothy C; Wall, Michael E; Bock, Nicolas; Rubensson, Emanuel H; Djidjev, Hristo

2016-06-21

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

Graph-based linear scaling electronic structure theory

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

Niklasson, Anders M. N., E-mail: amn@lanl.gov; Negre, Christian F. A.; Cawkwell, Marc J.

2016-06-21

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

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

Shi, Xuanhua; Luo, Xuan; Liang, Junling

GPUs have been increasingly used to accelerate graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or atomic operations, leading to significant penalties/overheads when implemented on GPUs. As such, coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. Common coloring algorithms, however, may suffer from low parallelism because of a large number of colors generally required for processing a large-scale graph with billions of vertices. We propose a light-weightmore » asynchronous processing framework called Frog with a preprocessing/hybrid coloring model. The fundamental idea is based on Pareto principle (or 80-20 rule) about coloring algorithms as we observed through masses of realworld graph coloring cases. We find that a majority of vertices (about 80%) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution separates the processing of the vertices based on the distribution of colors. In this work, we mainly answer three questions: (1) how to partition the vertices in a sparse graph with maximized parallelism, (2) how to process large-scale graphs that cannot fit into GPU memory, and (3) how to reduce the overhead of data transfers on PCIe while processing each partition. We conduct experiments on real-world data (Amazon, DBLP, YouTube, RoadNet-CA, WikiTalk and Twitter) to evaluate our approach and make comparisons with well-known non-preprocessed (such as Totem, Medusa, MapGraph and Gunrock) and preprocessed (Cusha) approaches, by testing four classical algorithms (BFS, PageRank, SSSP and CC). On all the tested applications and datasets, Frog is able to significantly outperform existing GPU-based graph processing systems except Gunrock and MapGraph. MapGraph gets better performance than Frog when running BFS on RoadNet-CA. The comparison between Gunrock and Frog is inconclusive. Frog can outperform Gunrock more than 1.04X when running PageRank and SSSP, while the advantage of Frog is not obvious when running BFS and CC on some datasets especially for RoadNet-CA.« less

Efficient quantum pseudorandomness with simple graph states

NASA Astrophysics Data System (ADS)

Mezher, Rawad; Ghalbouni, Joe; Dgheim, Joseph; Markham, Damian

2018-02-01

Measurement based (MB) quantum computation allows for universal quantum computing by measuring individual qubits prepared in entangled multipartite states, known as graph states. Unless corrected for, the randomness of the measurements leads to the generation of ensembles of random unitaries, where each random unitary is identified with a string of possible measurement results. We show that repeating an MB scheme an efficient number of times, on a simple graph state, with measurements at fixed angles and no feedforward corrections, produces a random unitary ensemble that is an ɛ -approximate t design on n qubits. Unlike previous constructions, the graph is regular and is also a universal resource for measurement based quantum computing, closely related to the brickwork state.

Sclerenchymatous ring as a barrier to phloem feeding by Asian citrus psyllid: Evidence from electrical penetration graph and visualization of stylet pathways

USDA-ARS?s Scientific Manuscript database

Asian citrus psyllid (ACP) feeding behaviors play a significant role in the transmission of the phloem-limited Candidatus Liberibacter asiaticus (CLas) bacteria that cause citrus greening disease. Sustained phloem ingestion by ACP on CLas infected plants is very important in pathogen acquisition and...

Local Higher-Order Graph Clustering

PubMed Central

Yin, Hao; Benson, Austin R.; Leskovec, Jure; Gleich, David F.

2018-01-01

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a given seed node and are faster than traditional global graph clustering methods because their runtime does not depend on the size of the input graph. However, current local graph partitioning methods are not designed to account for the higher-order structures crucial to the network, nor can they effectively handle directed networks. Here we introduce a new class of local graph clustering methods that address these issues by incorporating higher-order network information captured by small subgraphs, also called network motifs. We develop the Motif-based Approximate Personalized PageRank (MAPPR) algorithm that finds clusters containing a seed node with minimal motif conductance, a generalization of the conductance metric for network motifs. We generalize existing theory to prove the fast running time (independent of the size of the graph) and obtain theoretical guarantees on the cluster quality (in terms of motif conductance). We also develop a theory of node neighborhoods for finding sets that have small motif conductance, and apply these results to the case of finding good seed nodes to use as input to the MAPPR algorithm. Experimental validation on community detection tasks in both synthetic and real-world networks, shows that our new framework MAPPR outperforms the current edge-based personalized PageRank methodology. PMID:29770258

Discovery of Tyk2 inhibitors via the virtual site-directed fragment-based drug design.

PubMed

Jang, Woo Dae; Kim, Jun-Tae; Son, Hoon Young; Park, Seung Yeon; Cho, Young Sik; Koo, Tae-sung; Lee, Hyuk; Kang, Nam Sook

2015-09-15

In this study, we synthesized compound 12 with potent Tyk2 inhibitory activity from FBDD study and carried out a cell-based assay for Tyk2/STAT3 signaling activation upon IFNα5 stimulation. Compound 12 completely suppressed the IFNα5-mediated Tyk2/STAT3 signaling pathway as well as the basal levels of pSTAT3. Stimulation with IFNα/β leads to the tyrosine phosphorylation of the JAK1 and Tyk2 receptor-associated kinases with subsequent STATs activation, transmitting signals from the cell surface receptor to the nucleus. In conclusion, the potency of compound 12 to interrupt the signal transmission of Tyk2/STAT3 appeared to be equivalent or superior to that of the reference compound. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using graph approach for managing connectivity in integrative landscape modelling

NASA Astrophysics Data System (ADS)

Rabotin, Michael; Fabre, Jean-Christophe; Libres, Aline; Lagacherie, Philippe; Crevoisier, David; Moussa, Roger

2013-04-01

In cultivated landscapes, a lot of landscape elements such as field boundaries, ditches or banks strongly impact water flows, mass and energy fluxes. At the watershed scale, these impacts are strongly conditionned by the connectivity of these landscape elements. An accurate representation of these elements and of their complex spatial arrangements is therefore of great importance for modelling and predicting these impacts.We developped in the framework of the OpenFLUID platform (Software Environment for Modelling Fluxes in Landscapes) a digital landscape representation that takes into account the spatial variabilities and connectivities of diverse landscape elements through the application of the graph theory concepts. The proposed landscape representation consider spatial units connected together to represent the flux exchanges or any other information exchanges. Each spatial unit of the landscape is represented as a node of a graph and relations between units as graph connections. The connections are of two types - parent-child connection and up/downstream connection - which allows OpenFLUID to handle hierarchical graphs. Connections can also carry informations and graph evolution during simulation is possible (connections or elements modifications). This graph approach allows a better genericity on landscape representation, a management of complex connections and facilitate development of new landscape representation algorithms. Graph management is fully operational in OpenFLUID for developers or modelers ; and several graph tools are available such as graph traversal algorithms or graph displays. Graph representation can be managed i) manually by the user (for example in simple catchments) through XML-based files in easily editable and readable format or ii) by using methods of the OpenFLUID-landr library which is an OpenFLUID library relying on common open-source spatial libraries (ogr vector, geos topologic vector and gdal raster libraries). OpenFLUID-landr library has been developed in order i) to be used with no GIS expert skills needed (common gis formats can be read and simplified spatial management is provided), ii) to easily develop adapted rules of landscape discretization and graph creation to follow spatialized model requirements and iii) to allow model developers to manage dynamic and complex spatial topology. Graph management in OpenFLUID are shown with i) examples of hydrological modelizations on complex farmed landscapes and ii) the new implementation of Geo-MHYDAS tool based on the OpenFLUID-landr library, which allows to discretize a landscape and create graph structure for the MHYDAS model requirements.

A preliminary study on atrial epicardial mapping signals based on Graph Theory.

PubMed

Sun, Liqian; Yang, Cuiwei; Zhang, Lin; Chen, Ying; Wu, Zhong; Shao, Jun

2014-07-01

In order to get a better understanding of atrial fibrillation, we introduced a method based on Graph Theory to interpret the relations of different parts of the atria. Atrial electrograms under sinus rhythm and atrial fibrillation were collected from eight living mongrel dogs with cholinergic AF model. These epicardial signals were acquired from 95 unipolar electrodes attached to the surface of the atria and four pulmonary veins. Then, we analyzed the electrode correlations using Graph Theory. The topology, the connectivity and the parameters of graphs during different rhythms were studied. Our results showed that the connectivity of graphs varied from sinus rhythm to atrial fibrillation and there were parameter gradients in various parts of the atria. The results provide spatial insight into the interaction between different parts of the atria and the method may have its potential for studying atrial fibrillation. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Fault-tolerant dynamic task graph scheduling

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

Kurt, Mehmet C.; Krishnamoorthy, Sriram; Agrawal, Kunal

2014-11-16

In this paper, we present an approach to fault tolerant execution of dynamic task graphs scheduled using work stealing. In particular, we focus on selective and localized recovery of tasks in the presence of soft faults. We elicit from the user the basic task graph structure in terms of successor and predecessor relationships. The work stealing-based algorithm to schedule such a task graph is augmented to enable recovery when the data and meta-data associated with a task get corrupted. We use this redundancy, and the knowledge of the task graph structure, to selectively recover from faults with low space andmore » time overheads. We show that the fault tolerant design retains the essential properties of the underlying work stealing-based task scheduling algorithm, and that the fault tolerant execution is asymptotically optimal when task re-execution is taken into account. Experimental evaluation demonstrates the low cost of recovery under various fault scenarios.« less

Path similarity skeleton graph matching.

PubMed

Bai, Xiang; Latecki, Longin Jan

2008-07-01

This paper presents a novel framework to for shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the shortest paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we completely ignore the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of shortest paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned by contour partitioning with Discrete Curve Evolution, which implies that the endpoints of skeleton branches correspond to visual parts of the objects. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and occlusion.

Greenberger-Horne-Zeilinger paradoxes from qudit graph states.

PubMed

Tang, Weidong; Yu, Sixia; Oh, C H

2013-03-08

One fascinating way of revealing quantum nonlocality is the all-versus-nothing test due to Greenberger, Horne, and Zeilinger (GHZ) known as the GHZ paradox. So far genuine multipartite and multilevel GHZ paradoxes are known to exist only in systems containing an odd number of particles. Here we shall construct GHZ paradoxes for an arbitrary number (greater than 3) of particles with the help of qudit graph states on a special kind of graphs, called GHZ graphs. Furthermore, based on the GHZ paradox arising from a GHZ graph, we derive a Bell inequality with two d-outcome observables for each observer, whose maximal violation attained by the corresponding graph state, and a Kochen-Specker inequality testing the quantum contextuality in a state-independent fashion.

Augmented multivariate image analysis applied to quantitative structure-activity relationship modeling of the phytotoxicities of benzoxazinone herbicides and related compounds on problematic weeds.

PubMed

Freitas, Mirlaine R; Matias, Stella V B G; Macedo, Renato L G; Freitas, Matheus P; Venturin, Nelson

2013-09-11

Two of major weeds affecting cereal crops worldwide are Avena fatua L. (wild oat) and Lolium rigidum Gaud. (rigid ryegrass). Thus, development of new herbicides against these weeds is required; in line with this, benzoxazinones, their degradation products, and analogues have been shown to be important allelochemicals and natural herbicides. Despite earlier structure-activity studies demonstrating that hydrophobicity (log P) of aminophenoxazines correlates to phytotoxicity, our findings for a series of benzoxazinone derivatives do not show any relationship between phytotoxicity and log P nor with other two usual molecular descriptors. On the other hand, a quantitative structure-activity relationship (QSAR) analysis based on molecular graphs representing structural shape, atomic sizes, and colors to encode other atomic properties performed very accurately for the prediction of phytotoxicities of these compounds against wild oat and rigid ryegrass. Therefore, these QSAR models can be used to estimate the phytotoxicity of new congeners of benzoxazinone herbicides toward A. fatua L. and L. rigidum Gaud.

Phylum-wide comparative genomics unravel the diversity of secondary metabolism in Cyanobacteria

DOE PAGES

Calteau, Alexandra; Fewer, David P.; Latifi, Amel; ...

2014-11-18

Cyanobacteria are an ancient lineage of photosynthetic bacteria from which hundreds of natural products have been described, including many notorious toxins but also potent natural products of interest to the pharmaceutical and biotechnological industries. Many of these compounds are the products of non-ribosomal peptide synthetase (NRPS) or polyketide synthase (PKS) pathways. However, current understanding of the diversification of these pathways is largely based on the chemical structure of the bioactive compounds, while the evolutionary forces driving their remarkable chemical diversity are poorly understood. We carried out a phylum-wide investigation of genetic diversification of the cyanobacterial NRPS and PKS pathways formore » the production of bioactive compounds. 452 NRPS and PKS gene clusters were identified from 89 cyanobacterial genomes, revealing a clear burst in late-branching lineages. Our genomic analysis further grouped the clusters into 286 highly diversified cluster families (CF) of pathways. Some CFs appeared vertically inherited, while others presented a more complex evolutionary history. Only a few horizontal gene transfers were evidenced amongst strongly conserved CFs in the phylum, while several others have undergone drastic gene shuffling events, which could result in the observed diversification of the pathways. In addition to toxin production, several NRPS and PKS gene clusters are devoted to important cellular processes of these bacteria such as nitrogen fixation and iron uptake. The majority of the biosynthetic clusters identified here have unknown end products, highlighting the power of genome mining for the discovery of new natural products.« less

Phylum-wide comparative genomics unravel the diversity of secondary metabolism in Cyanobacteria

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

Calteau, Alexandra; Fewer, David P.; Latifi, Amel

Cyanobacteria are an ancient lineage of photosynthetic bacteria from which hundreds of natural products have been described, including many notorious toxins but also potent natural products of interest to the pharmaceutical and biotechnological industries. Many of these compounds are the products of non-ribosomal peptide synthetase (NRPS) or polyketide synthase (PKS) pathways. However, current understanding of the diversification of these pathways is largely based on the chemical structure of the bioactive compounds, while the evolutionary forces driving their remarkable chemical diversity are poorly understood. We carried out a phylum-wide investigation of genetic diversification of the cyanobacterial NRPS and PKS pathways formore » the production of bioactive compounds. 452 NRPS and PKS gene clusters were identified from 89 cyanobacterial genomes, revealing a clear burst in late-branching lineages. Our genomic analysis further grouped the clusters into 286 highly diversified cluster families (CF) of pathways. Some CFs appeared vertically inherited, while others presented a more complex evolutionary history. Only a few horizontal gene transfers were evidenced amongst strongly conserved CFs in the phylum, while several others have undergone drastic gene shuffling events, which could result in the observed diversification of the pathways. In addition to toxin production, several NRPS and PKS gene clusters are devoted to important cellular processes of these bacteria such as nitrogen fixation and iron uptake. The majority of the biosynthetic clusters identified here have unknown end products, highlighting the power of genome mining for the discovery of new natural products.« less

Reaction pathways for the deoxygenation of vegetable oils and related model compounds.

PubMed

Gosselink, Robert W; Hollak, Stefan A W; Chang, Shu-Wei; van Haveren, Jacco; de Jong, Krijn P; Bitter, Johannes H; van Es, Daan S

2013-09-01

Vegetable oil-based feeds are regarded as an alternative source for the production of fuels and chemicals. Paraffins and olefins can be produced from these feeds through catalytic deoxygenation. The fundamentals of this process are mostly studied by using model compounds such as fatty acids, fatty acid esters, and specific triglycerides because of their structural similarity to vegetable oils. In this Review we discuss the impact of feedstock, reaction conditions, and nature of the catalyst on the reaction pathways of the deoxygenation of vegetable oils and its derivatives. As such, we conclude on the suitability of model compounds for this reaction. It is shown that the type of catalyst has a significant effect on the deoxygenation pathway, that is, group 10 metal catalysts are active in decarbonylation/decarboxylation whereas metal sulfide catalysts are more selective to hydrodeoxygenation. Deoxygenation studies performed under H2 showed similar pathways for fatty acids, fatty acid esters, triglycerides, and vegetable oils, as mostly deoxygenation occurs indirectly via the formation of fatty acids. Deoxygenation in the absence of H2 results in significant differences in reaction pathways and selectivities depending on the feedstock. Additionally, using unsaturated feedstocks under inert gas results in a high selectivity to undesired reactions such as cracking and the formation of heavies. Therefore, addition of H2 is proposed to be essential for the catalytic deoxygenation of vegetable oil feeds. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A global/local affinity graph for image segmentation.

PubMed

Xiaofang Wang; Yuxing Tang; Masnou, Simon; Liming Chen

2015-04-01

Construction of a reliable graph capturing perceptual grouping cues of an image is fundamental for graph-cut based image segmentation methods. In this paper, we propose a novel sparse global/local affinity graph over superpixels of an input image to capture both short- and long-range grouping cues, and thereby enabling perceptual grouping laws, including proximity, similarity, continuity, and to enter in action through a suitable graph-cut algorithm. Moreover, we also evaluate three major visual features, namely, color, texture, and shape, for their effectiveness in perceptual segmentation and propose a simple graph fusion scheme to implement some recent findings from psychophysics, which suggest combining these visual features with different emphases for perceptual grouping. In particular, an input image is first oversegmented into superpixels at different scales. We postulate a gravitation law based on empirical observations and divide superpixels adaptively into small-, medium-, and large-sized sets. Global grouping is achieved using medium-sized superpixels through a sparse representation of superpixels' features by solving a ℓ0-minimization problem, and thereby enabling continuity or propagation of local smoothness over long-range connections. Small- and large-sized superpixels are then used to achieve local smoothness through an adjacent graph in a given feature space, and thus implementing perceptual laws, for example, similarity and proximity. Finally, a bipartite graph is also introduced to enable propagation of grouping cues between superpixels of different scales. Extensive experiments are carried out on the Berkeley segmentation database in comparison with several state-of-the-art graph constructions. The results show the effectiveness of the proposed approach, which outperforms state-of-the-art graphs using four different objective criteria, namely, the probabilistic rand index, the variation of information, the global consistency error, and the boundary displacement error.

S-aryl-L-cysteine sulphoxides and related organosulphur compounds alter oral biofilm development and AI-2-based cell-cell communication.

PubMed

Kasper, S H; Samarian, D; Jadhav, A P; Rickard, A H; Musah, R A; Cady, N C

2014-11-01

To design and synthesize a library of structurally related, small molecules related to homologues of compounds produced by the plant Petiveria alliacea and determine their ability to interfere with AI-2 cell-cell communication and biofilm formation by oral bacteria. Many human diseases are associated with persistent bacterial biofilms. Oral biofilms (dental plaque) are problematic as they are often associated with tooth decay, periodontal disease and systemic disorders such as heart disease and diabetes. Using a microplate-based approach, a bio-inspired small molecule library was screened for anti-biofilm activity against the oral species Streptococcus mutans UA159, Streptococcus sanguis 10556 and Actinomyces oris MG1. To complement the static screen, a flow-based BioFlux microfluidic system screen was also performed under conditions representative of the human oral cavity. Several compounds were found to display biofilm inhibitory activity in all three of the oral bacteria tested. These compounds were also shown to inhibit bioluminescence by Vibrio harveyi and were thus inferred to be quorum sensing (QS) inhibitors. Due to the structural similarity of these compounds to each other, and to key molecules in AI-2 biosynthetic pathways, we propose that these molecules potentially reduce biofilm formation via antagonism of QS or QS-related pathways. This study highlights the potential for a non-antimicrobial-based strategy, focused on AI-2 cell-cell signalling, to control the development of dental plaque. Considering that many bacterial species use AI-2 cell-cell signalling, as well as the increased concern of the use of antimicrobials in healthcare products, such an anti-biofilm approach could also be used to control biofilms in environments beyond the human oral cavity. © 2014 The Society for Applied Microbiology.

A nitrous acid biosynthetic pathway for diazo group formation in bacteria.

PubMed

Sugai, Yoshinori; Katsuyama, Yohei; Ohnishi, Yasuo

2016-02-01

Although some diazo compounds have bioactivities of medicinal interest, little is known about diazo group formation in nature. Here we describe an unprecedented nitrous acid biosynthetic pathway responsible for the formation of a diazo group in the biosynthesis of the ortho-diazoquinone secondary metabolite cremeomycin in Streptomyces cremeus. This finding provides important insights into the biosynthetic pathways not only for diazo compounds but also for other naturally occurring compounds containing nitrogen-nitrogen bonds.

DELTACON: A Principled Massive-Graph Similarity Function with Attribution

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

Koutra, Danai; Shah, Neil; Vogelstein, Joshua T.

How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customersmore » of a mobile carrier). In our experiments on various synthetic and real graphs we showcase the advantages of our method over existing similarity measures. We also employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.« less

DELTACON: A Principled Massive-Graph Similarity Function with Attribution

DOE PAGES

Koutra, Danai; Shah, Neil; Vogelstein, Joshua T.; ...

2014-05-22

How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customersmore » of a mobile carrier). In our experiments on various synthetic and real graphs we showcase the advantages of our method over existing similarity measures. We also employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.« less

Measuring Graph Comprehension, Critique, and Construction in Science

NASA Astrophysics Data System (ADS)

Lai, Kevin; Cabrera, Julio; Vitale, Jonathan M.; Madhok, Jacquie; Tinker, Robert; Linn, Marcia C.

2016-08-01

Interpreting and creating graphs plays a critical role in scientific practice. The K-12 Next Generation Science Standards call for students to use graphs for scientific modeling, reasoning, and communication. To measure progress on this dimension, we need valid and reliable measures of graph understanding in science. In this research, we designed items to measure graph comprehension, critique, and construction and developed scoring rubrics based on the knowledge integration (KI) framework. We administered the items to over 460 middle school students. We found that the items formed a coherent scale and had good reliability using both item response theory and classical test theory. The KI scoring rubric showed that most students had difficulty linking graphs features to science concepts, especially when asked to critique or construct graphs. In addition, students with limited access to computers as well as those who speak a language other than English at home have less integrated understanding than others. These findings point to the need to increase the integration of graphing into science instruction. The results suggest directions for further research leading to comprehensive assessments of graph understanding.

Engineering tolerance to industrially relevant stress factors in yeast cell factories.

PubMed

Deparis, Quinten; Claes, Arne; Foulquié-Moreno, Maria R; Thevelein, Johan M

2017-06-01

The main focus in development of yeast cell factories has generally been on establishing optimal activity of heterologous pathways and further metabolic engineering of the host strain to maximize product yield and titer. Adequate stress tolerance of the host strain has turned out to be another major challenge for obtaining economically viable performance in industrial production. Although general robustness is a universal requirement for industrial microorganisms, production of novel compounds using artificial metabolic pathways presents additional challenges. Many of the bio-based compounds desirable for production by cell factories are highly toxic to the host cells in the titers required for economic viability. Artificial metabolic pathways also turn out to be much more sensitive to stress factors than endogenous pathways, likely because regulation of the latter has been optimized in evolution in myriads of environmental conditions. We discuss different environmental and metabolic stress factors with high relevance for industrial utilization of yeast cell factories and the experimental approaches used to engineer higher stress tolerance. Improving stress tolerance in a predictable manner in yeast cell factories should facilitate their widespread utilization in the bio-based economy and extend the range of products successfully produced in large scale in a sustainable and economically profitable way. © FEMS 2017.

Engineering tolerance to industrially relevant stress factors in yeast cell factories

PubMed Central

Deparis, Quinten; Claes, Arne; Foulquié-Moreno, Maria R.

2017-01-01

Abstract The main focus in development of yeast cell factories has generally been on establishing optimal activity of heterologous pathways and further metabolic engineering of the host strain to maximize product yield and titer. Adequate stress tolerance of the host strain has turned out to be another major challenge for obtaining economically viable performance in industrial production. Although general robustness is a universal requirement for industrial microorganisms, production of novel compounds using artificial metabolic pathways presents additional challenges. Many of the bio-based compounds desirable for production by cell factories are highly toxic to the host cells in the titers required for economic viability. Artificial metabolic pathways also turn out to be much more sensitive to stress factors than endogenous pathways, likely because regulation of the latter has been optimized in evolution in myriads of environmental conditions. We discuss different environmental and metabolic stress factors with high relevance for industrial utilization of yeast cell factories and the experimental approaches used to engineer higher stress tolerance. Improving stress tolerance in a predictable manner in yeast cell factories should facilitate their widespread utilization in the bio-based economy and extend the range of products successfully produced in large scale in a sustainable and economically profitable way. PMID:28586408

Top-k similar graph matching using TraM in biological networks.

PubMed

Amin, Mohammad Shafkat; Finley, Russell L; Jamil, Hasan M

2012-01-01

Many emerging database applications entail sophisticated graph-based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact subgraph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. In this paper, we propose a novel technique called TraM for approximate graph matching that off-loads a significant amount of its processing on to the database making the approach viable for large graphs. Moreover, the vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We annotate nodes of the query graphs by means of their global topological properties and compare them with neighborhood biased segments of the datagraph for proper matches. We have conducted experiments on several real data sets, and have demonstrated the effectiveness and efficiency of the proposed method

On the modification Highly Connected Subgraphs (HCS) algorithm in graph clustering for weighted graph

NASA Astrophysics Data System (ADS)

Albirri, E. R.; Sugeng, K. A.; Aldila, D.

2018-04-01

Nowadays, in the modern world, since technology and human civilization start to progress, all city in the world is almost connected. The various places in this world are easier to visit. It is an impact of transportation technology and highway construction. The cities which have been connected can be represented by graph. Graph clustering is one of ways which is used to answer some problems represented by graph. There are some methods in graph clustering to solve the problem spesifically. One of them is Highly Connected Subgraphs (HCS) method. HCS is used to identify cluster based on the graph connectivity k for graph G. The connectivity in graph G is denoted by k(G)> \\frac{n}{2} that n is the total of vertices in G, then it is called as HCS or the cluster. This research used literature review and completed with simulation of program in a software. We modified HCS algorithm by using weighted graph. The modification is located in the Process Phase. Process Phase is used to cut the connected graph G into two subgraphs H and \\bar{H}. We also made a program by using software Octave-401. Then we applied the data of Flight Routes Mapping of One of Airlines in Indonesia to our program.

Use of Graph Database for the Integration of Heterogeneous Biological Data.

PubMed

Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young

2017-03-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

Use of Graph Database for the Integration of Heterogeneous Biological Data

PubMed Central

Yoon, Byoung-Ha; Kim, Seon-Kyu

2017-01-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data. PMID:28416946

Investigating the different mechanisms of genotoxic and non-genotoxic carcinogens by a gene set analysis.

PubMed

Lee, Won Jun; Kim, Sang Cheol; Lee, Seul Ji; Lee, Jeongmi; Park, Jeong Hill; Yu, Kyung-Sang; Lim, Johan; Kwon, Sung Won

2014-01-01

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways.

Investigating the Different Mechanisms of Genotoxic and Non-Genotoxic Carcinogens by a Gene Set Analysis

PubMed Central

Lee, Won Jun; Kim, Sang Cheol; Lee, Seul Ji; Lee, Jeongmi; Park, Jeong Hill; Yu, Kyung-Sang; Lim, Johan; Kwon, Sung Won

2014-01-01

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways. PMID:24497971

Epigenetic Library Screen Identifies Abexinostat as Novel Regulator of Adipocytic and Osteoblastic Differentiation of Human Skeletal (Mesenchymal) Stem Cells

PubMed Central

Ali, Dalia; Hamam, Rimi; Alfayez, Musaed; Kassem, Moustapha; Aldahmash, Abdullah

2016-01-01

The epigenetic mechanisms promoting lineage-specific commitment of human skeletal (mesenchymal or stromal) stem cells (hMSCs) into adipocytes or osteoblasts are still not fully understood. Herein, we performed an epigenetic library functional screen and identified several novel compounds, including abexinostat, which promoted adipocytic and osteoblastic differentiation of hMSCs. Using gene expression microarrays, chromatin immunoprecipitation for H3K9Ac combined with high-throughput DNA sequencing (ChIP-seq), and bioinformatics, we identified several key genes involved in regulating stem cell proliferation and differentiation that were targeted by abexinostat. Concordantly, ChIP-quantitative polymerase chain reaction revealed marked increase in H3K9Ac epigenetic mark on the promoter region of AdipoQ, FABP4, PPARγ, KLF15, CEBPA, SP7, and ALPL in abexinostat-treated hMSCs. Pharmacological inhibition of focal adhesion kinase (PF-573228) or insulin-like growth factor-1R/insulin receptor (NVP-AEW51) signaling exhibited significant inhibition of abexinostat-mediated adipocytic differentiation, whereas inhibition of WNT (XAV939) or transforming growth factor-β (SB505124) signaling abrogated abexinostat-mediated osteogenic differentiation of hMSCs. Our findings provide insight into the understanding of the relationship between the epigenetic effect of histone deacetylase inhibitors, transcription factors, and differentiation pathways governing adipocyte and osteoblast differentiation. Manipulating such pathways allows a novel use for epigenetic compounds in hMSC-based therapies and tissue engineering. Significance This unbiased epigenetic library functional screen identified several novel compounds, including abexinostat, that promoted adipocytic and osteoblastic differentiation of human skeletal (mesenchymal or stromal) stem cells (hMSCs). These data provide new insight into the understanding of the relationship between the epigenetic effect of histone deacetylase inhibitors, transcription factors, and differentiation pathways controlling adipocyte and osteoblast differentiation of hMSCs. Manipulating such pathways allows a novel use for epigenetic compounds in hMSC-based therapies for tissue engineering, bone disease, obesity, and metabolic-disorders. PMID:27194745

A comparison of cationic polymerization and esterification for end-point detection in the catalytic thermometric titration of organic bases.

PubMed

J Greenhow, E; Viñas, P

1984-08-01

A systematic comparison has been made of two indicator systems for the non-aqueous catalytic thermometric titration of strong and weak organic bases. The indicator reagents, alpha-methylstyrene and mixtures of acetic anhydride and hydroxy compounds, are shown to give results (for 14 representative bases) which do not diner significantly in coefficient of variation or titration error. Calibration graphs for all the samples, in the range 0.01-0.1 meq, are linear, with correlation coefficients of 0.995 or better. Aniline, benzylamine, n-butylamine, morpholine, pyrrole, l-dopa, alpha-methyl-l-dopa, dl-alpha-alanine, dl-leucine and l-cysteine cannot be determined when acetic anhydride is present in the sample solution, but some primary and second amines can. This is explained in terms of rates of acetylation of the amino groups.

On a programming language for graph algorithms

NASA Technical Reports Server (NTRS)

Rheinboldt, W. C.; Basili, V. R.; Mesztenyi, C. K.

1971-01-01

An algorithmic language, GRAAL, is presented for describing and implementing graph algorithms of the type primarily arising in applications. The language is based on a set algebraic model of graph theory which defines the graph structure in terms of morphisms between certain set algebraic structures over the node set and arc set. GRAAL is modular in the sense that the user specifies which of these mappings are available with any graph. This allows flexibility in the selection of the storage representation for different graph structures. In line with its set theoretic foundation, the language introduces sets as a basic data type and provides for the efficient execution of all set and graph operators. At present, GRAAL is defined as an extension of ALGOL 60 (revised) and its formal description is given as a supplement to the syntactic and semantic definition of ALGOL. Several typical graph algorithms are written in GRAAL to illustrate various features of the language and to show its applicability.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

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

Graph-based layout analysis for PDF documents

NASA Astrophysics Data System (ADS)

Xu, Canhui; Tang, Zhi; Tao, Xin; Li, Yun; Shi, Cao

2013-03-01

To increase the flexibility and enrich the reading experience of e-book on small portable screens, a graph based method is proposed to perform layout analysis on Portable Document Format (PDF) documents. Digital born document has its inherent advantages like representing texts and fractional images in explicit form, which can be straightforwardly exploited. To integrate traditional image-based document analysis and the inherent meta-data provided by PDF parser, the page primitives including text, image and path elements are processed to produce text and non text layer for respective analysis. Graph-based method is developed in superpixel representation level, and page text elements corresponding to vertices are used to construct an undirected graph. Euclidean distance between adjacent vertices is applied in a top-down manner to cut the graph tree formed by Kruskal's algorithm. And edge orientation is then used in a bottom-up manner to extract text lines from each sub tree. On the other hand, non-textual objects are segmented by connected component analysis. For each segmented text and non-text composite, a 13-dimensional feature vector is extracted for labelling purpose. The experimental results on selected pages from PDF books are presented.

Memoryless cooperative graph search based on the simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Hou, Jian; Yan, Gang-Feng; Fan, Zhen

2011-04-01

We have studied the problem of reaching a globally optimal segment for a graph-like environment with a single or a group of autonomous mobile agents. Firstly, two efficient simulated-annealing-like algorithms are given for a single agent to solve the problem in a partially known environment and an unknown environment, respectively. It shows that under both proposed control strategies, the agent will eventually converge to a globally optimal segment with probability 1. Secondly, we use multi-agent searching to simultaneously reduce the computation complexity and accelerate convergence based on the algorithms we have given for a single agent. By exploiting graph partition, a gossip-consensus method based scheme is presented to update the key parameter—radius of the graph, ensuring that the agents spend much less time finding a globally optimal segment.

Scale Construction for Graphing: An Investigation of Students' Resources

ERIC Educational Resources Information Center

Delgado, Cesar; Lucero, Margaret M.

2015-01-01

Graphing is a fundamental part of the scientific process. Scales are key but little-studied components of graphs. Adopting a resources-based framework of cognitive structure, we identify the potential intuitive resources that six undergraduates of diverse majors and years at a public US research university activated when constructing scales, and…

The combination of direct and paired link graphs can boost repetitive genome assembly

PubMed Central

Shi, Wenyu; Ji, Peifeng

2017-01-01

Abstract Currently, most paired link based scaffolding algorithms intrinsically mask the sequences between two linked contigs and bypass their direct link information embedded in the original de Bruijn assembly graph. Such disadvantage substantially complicates the scaffolding process and leads to the inability of resolving repetitive contig assembly. Here we present a novel algorithm, inGAP-sf, for effectively generating high-quality and continuous scaffolds. inGAP-sf achieves this by using a new strategy based on the combination of direct link and paired link graphs, in which direct link is used to increase graph connectivity and to decrease graph complexity and paired link is employed to supervise the traversing process on the direct link graph. Such advantage greatly facilitates the assembly of short-repeat enriched regions. Moreover, a new comprehensive decision model is developed to eliminate the noise routes accompanying with the introduced direct link. Through extensive evaluations on both simulated and real datasets, we demonstrated that inGAP-sf outperforms most of the genome scaffolding algorithms by generating more accurate and continuous assembly, especially for short repetitive regions. PMID:27924003

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection.

PubMed

Yoon, Jeongah; Si, Yaguang; Nolan, Ryan; Lee, Kyongbum

2007-09-15

The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism. Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism. Supplementary data are available at Bioinformatics online.

Multiparty Quantum Blind Signature Scheme Based on Graph States

NASA Astrophysics Data System (ADS)

Jian-Wu, Liang; Xiao-Shu, Liu; Jin-Jing, Shi; Ying, Guo

2018-05-01

A multiparty quantum blind signature scheme is proposed based on the principle of graph state, in which the unitary operations of graph state particles can be applied to generate the quantum blind signature and achieve verification. Different from the classical blind signature based on the mathematical difficulty, the scheme could guarantee not only the anonymity but also the unconditionally security. The analysis shows that the length of the signature generated in our scheme does not become longer as the number of signers increases, and it is easy to increase or decrease the number of signers.

A chemical genetic screen for mTOR pathway inhibitors based on 4E-BP-dependent nuclear accumulation of eIF4E.

PubMed

Livingstone, Mark; Larsson, Ola; Sukarieh, Rami; Pelletier, Jerry; Sonenberg, Nahum

2009-12-24

The signal transduction pathway wherein mTOR regulates cellular growth and proliferation is an active target for drug discovery. The search for new mTOR inhibitors has recently yielded a handful of promising compounds that hold therapeutic potential. This search has been limited by the lack of a high-throughput assay to monitor the phosphorylation of a direct rapamycin-sensitive mTOR substrate in cells. Here we describe a novel cell-based chemical genetic screen useful for efficiently monitoring mTOR signaling to 4E-BPs in response to stimuli. The screen is based on the nuclear accumulation of eIF4E, which occurs in a 4E-BP-dependent manner specifically upon inhibition of mTOR signaling. Using this assay in a small-scale screen, we have identified several compounds not previously known to inhibit mTOR signaling, demonstrating that this method can be adapted to larger screens. Copyright 2009 Elsevier Ltd. All rights reserved.

Nested Tracking Graphs

DOE PAGES

Lukasczyk, Jonas; Weber, Gunther; Maciejewski, Ross; ...

2017-06-01

Tracking graphs are a well established tool in topological analysis to visualize the evolution of components and their properties over time, i.e., when components appear, disappear, merge, and split. However, tracking graphs are limited to a single level threshold and the graphs may vary substantially even under small changes to the threshold. To examine the evolution of features for varying levels, users have to compare multiple tracking graphs without a direct visual link between them. We propose a novel, interactive, nested graph visualization based on the fact that the tracked superlevel set components for different levels are related to eachmore » other through their nesting hierarchy. This approach allows us to set multiple tracking graphs in context to each other and enables users to effectively follow the evolution of components for different levels simultaneously. We show the effectiveness of our approach on datasets from finite pointset methods, computational fluid dynamics, and cosmology simulations.« less

Laplacian Estrada and normalized Laplacian Estrada indices of evolving graphs.

PubMed

Shang, Yilun

2015-01-01

Large-scale time-evolving networks have been generated by many natural and technological applications, posing challenges for computation and modeling. Thus, it is of theoretical and practical significance to probe mathematical tools tailored for evolving networks. In this paper, on top of the dynamic Estrada index, we study the dynamic Laplacian Estrada index and the dynamic normalized Laplacian Estrada index of evolving graphs. Using linear algebra techniques, we established general upper and lower bounds for these graph-spectrum-based invariants through a couple of intuitive graph-theoretic measures, including the number of vertices or edges. Synthetic random evolving small-world networks are employed to show the relevance of the proposed dynamic Estrada indices. It is found that neither the static snapshot graphs nor the aggregated graph can approximate the evolving graph itself, indicating the fundamental difference between the static and dynamic Estrada indices.

Generalizing a categorization of students' interpretations of linear kinematics graphs

NASA Astrophysics Data System (ADS)

Bollen, Laurens; De Cock, Mieke; Zuza, Kristina; Guisasola, Jenaro; van Kampen, Paul

2016-06-01

We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven) and the Basque Country, Spain (University of the Basque Country). We discuss how we adapted the categorization to accommodate a much more diverse student cohort and explain how the prior knowledge of students may account for many differences in the prevalence of approaches and success rates. Although calculus-based physics students make fewer mistakes than algebra-based physics students, they encounter similar difficulties that are often related to incorrectly dividing two coordinates. We verified that a qualitative understanding of kinematics is an important but not sufficient condition for students to determine a correct value for the speed. When comparing responses to questions on linear distance-time graphs with responses to isomorphic questions on linear water level versus time graphs, we observed that the context of a question influences the approach students use. Neither qualitative understanding nor an ability to find the slope of a context-free graph proved to be a reliable predictor for the approach students use when they determine the instantaneous speed.

Solving Hard Computational Problems Efficiently: Asymptotic Parametric Complexity 3-Coloring Algorithm

PubMed Central

Martín H., José Antonio

2013-01-01

Many practical problems in almost all scientific and technological disciplines have been classified as computationally hard (NP-hard or even NP-complete). In life sciences, combinatorial optimization problems frequently arise in molecular biology, e.g., genome sequencing; global alignment of multiple genomes; identifying siblings or discovery of dysregulated pathways. In almost all of these problems, there is the need for proving a hypothesis about certain property of an object that can be present if and only if it adopts some particular admissible structure (an NP-certificate) or be absent (no admissible structure), however, none of the standard approaches can discard the hypothesis when no solution can be found, since none can provide a proof that there is no admissible structure. This article presents an algorithm that introduces a novel type of solution method to “efficiently” solve the graph 3-coloring problem; an NP-complete problem. The proposed method provides certificates (proofs) in both cases: present or absent, so it is possible to accept or reject the hypothesis on the basis of a rigorous proof. It provides exact solutions and is polynomial-time (i.e., efficient) however parametric. The only requirement is sufficient computational power, which is controlled by the parameter . Nevertheless, here it is proved that the probability of requiring a value of to obtain a solution for a random graph decreases exponentially: , making tractable almost all problem instances. Thorough experimental analyses were performed. The algorithm was tested on random graphs, planar graphs and 4-regular planar graphs. The obtained experimental results are in accordance with the theoretical expected results. PMID:23349711

A New Analysis of Resting State Connectivity and Graph Theory Reveals Distinctive Short-Term Modulations due to Whisker Stimulation in Rats.

PubMed

Kreitz, Silke; de Celis Alonso, Benito; Uder, Michael; Hess, Andreas

2018-01-01

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short-term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions.

A New Analysis of Resting State Connectivity and Graph Theory Reveals Distinctive Short-Term Modulations due to Whisker Stimulation in Rats

PubMed Central

Kreitz, Silke; de Celis Alonso, Benito; Uder, Michael; Hess, Andreas

2018-01-01

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short-term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions. PMID:29875622

Protein domain organisation: adding order.

PubMed

Kummerfeld, Sarah K; Teichmann, Sarah A

2009-01-29

Domains are the building blocks of proteins. During evolution, they have been duplicated, fused and recombined, to produce proteins with novel structures and functions. Structural and genome-scale studies have shown that pairs or groups of domains observed together in a protein are almost always found in only one N to C terminal order and are the result of a single recombination event that has been propagated by duplication of the multi-domain unit. Previous studies of domain organisation have used graph theory to represent the co-occurrence of domains within proteins. We build on this approach by adding directionality to the graphs and connecting nodes based on their relative order in the protein. Most of the time, the linear order of domains is conserved. However, using the directed graph representation we have identified non-linear features of domain organization that are over-represented in genomes. Recognising these patterns and unravelling how they have arisen may allow us to understand the functional relationships between domains and understand how the protein repertoire has evolved. We identify groups of domains that are not linearly conserved, but instead have been shuffled during evolution so that they occur in multiple different orders. We consider 192 genomes across all three kingdoms of life and use domain and protein annotation to understand their functional significance. To identify these features and assess their statistical significance, we represent the linear order of domains in proteins as a directed graph and apply graph theoretical methods. We describe two higher-order patterns of domain organisation: clusters and bi-directionally associated domain pairs and explore their functional importance and phylogenetic conservation. Taking into account the order of domains, we have derived a novel picture of global protein organization. We found that all genomes have a higher than expected degree of clustering and more domain pairs in forward and reverse orientation in different proteins relative to random graphs with identical degree distributions. While these features were statistically over-represented, they are still fairly rare. Looking in detail at the proteins involved, we found strong functional relationships within each cluster. In addition, the domains tended to be involved in protein-protein interaction and are able to function as independent structural units. A particularly striking example was the human Jak-STAT signalling pathway which makes use of a set of domains in a range of orders and orientations to provide nuanced signaling functionality. This illustrated the importance of functional and structural constraints (or lack thereof) on domain organisation.

Functional grouping of similar genes using eigenanalysis on minimum spanning tree based neighborhood graph.

PubMed

Jothi, R; Mohanty, Sraban Kumar; Ojha, Aparajita

2016-04-01

Gene expression data clustering is an important biological process in DNA microarray analysis. Although there have been many clustering algorithms for gene expression analysis, finding a suitable and effective clustering algorithm is always a challenging problem due to the heterogeneous nature of gene profiles. Minimum Spanning Tree (MST) based clustering algorithms have been successfully employed to detect clusters of varying shapes and sizes. This paper proposes a novel clustering algorithm using Eigenanalysis on Minimum Spanning Tree based neighborhood graph (E-MST). As MST of a set of points reflects the similarity of the points with their neighborhood, the proposed algorithm employs a similarity graph obtained from k(') rounds of MST (k(')-MST neighborhood graph). By studying the spectral properties of the similarity matrix obtained from k(')-MST graph, the proposed algorithm achieves improved clustering results. We demonstrate the efficacy of the proposed algorithm on 12 gene expression datasets. Experimental results show that the proposed algorithm performs better than the standard clustering algorithms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Topological Characterization of Carbon Graphite and Crystal Cubic Carbon Structures.

PubMed

Siddiqui, Wei Gao Muhammad Kamran; Naeem, Muhammad; Rehman, Najma Abdul

2017-09-07

Graph theory is used for modeling, designing, analysis and understanding chemical structures or chemical networks and their properties. The molecular graph is a graph consisting of atoms called vertices and the chemical bond between atoms called edges. In this article, we study the chemical graphs of carbon graphite and crystal structure of cubic carbon. Moreover, we compute and give closed formulas of degree based additive topological indices, namely hyper-Zagreb index, first multiple and second multiple Zagreb indices, and first and second Zagreb polynomials.

An Xdata Architecture for Federated Graph Models and Multi-tier Asymmetric Computing

DTIC Science & Technology

2014-01-01

Wikipedia, a scale-free random graph (kron), Akamai trace route data, Bitcoin transaction data, and a Twitter follower network. We present results for...3x (SSSP on a random graph) and nearly 300x (Akamai and Bitcoin ) over the CPU performance of a well-known and widely deployed CPU-based graph...provided better throughput for smaller frontiers such as roadmaps or the Bitcoin data set. In our work, we have focused on two-phase kernels, but it

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

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

Demeure, I.M.

The research presented here is concerned with representation techniques and tools to support the design, prototyping, simulation, and evaluation of message-based parallel, distributed computations. The author describes ParaDiGM-Parallel, Distributed computation Graph Model-a visual representation technique for parallel, message-based distributed computations. ParaDiGM provides several views of a computation depending on the aspect of concern. It is made of two complementary submodels, the DCPG-Distributed Computing Precedence Graph-model, and the PAM-Process Architecture Model-model. DCPGs are precedence graphs used to express the functionality of a computation in terms of tasks, message-passing, and data. PAM graphs are used to represent the partitioning of a computationmore » into schedulable units or processes, and the pattern of communication among those units. There is a natural mapping between the two models. He illustrates the utility of ParaDiGM as a representation technique by applying it to various computations (e.g., an adaptive global optimization algorithm, the client-server model). ParaDiGM representations are concise. They can be used in documenting the design and the implementation of parallel, distributed computations, in describing such computations to colleagues, and in comparing and contrasting various implementations of the same computation. He then describes VISA-VISual Assistant, a software tool to support the design, prototyping, and simulation of message-based parallel, distributed computations. VISA is based on the ParaDiGM model. In particular, it supports the editing of ParaDiGM graphs to describe the computations of interest, and the animation of these graphs to provide visual feedback during simulations. The graphs are supplemented with various attributes, simulation parameters, and interpretations which are procedures that can be executed by VISA.« less

A Model of Knowledge Based Information Retrieval with Hierarchical Concept Graph.

ERIC Educational Resources Information Center

Kim, Young Whan; Kim, Jin H.

1990-01-01

Proposes a model of knowledge-based information retrieval (KBIR) that is based on a hierarchical concept graph (HCG) which shows relationships between index terms and constitutes a hierarchical thesaurus as a knowledge base. Conceptual distance between a query and an object is discussed and the use of Boolean operators is described. (25…

Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy.

PubMed

Bernhardt, Boris C; Bonilha, Leonardo; Gross, Donald W

2015-09-01

Recent years have witnessed a paradigm shift in the study and conceptualization of epilepsy, which is increasingly understood as a network-level disorder. An emblematic case is temporal lobe epilepsy (TLE), the most common drug-resistant epilepsy that is electroclinically defined as a focal epilepsy and pathologically associated with hippocampal sclerosis. In this review, we will summarize histopathological, electrophysiological, and neuroimaging evidence supporting the concept that the substrate of TLE is not limited to the hippocampus alone, but rather is broadly distributed across multiple brain regions and interconnecting white matter pathways. We will introduce basic concepts of graph theory, a formalism to quantify topological properties of complex systems that has recently been widely applied to study networks derived from brain imaging and electrophysiology. We will discuss converging graph theoretical evidence indicating that networks in TLE show marked shifts in their overall topology, providing insight into the neurobiology of TLE as a network-level disorder. Our review will conclude by discussing methodological challenges and future clinical applications of this powerful analytical approach. Copyright © 2015 Elsevier Inc. All rights reserved.

Anaerobic Catabolism of Aromatic Compounds: a Genetic and Genomic View

PubMed Central

Carmona, Manuel; Zamarro, María Teresa; Blázquez, Blas; Durante-Rodríguez, Gonzalo; Juárez, Javier F.; Valderrama, J. Andrés; Barragán, María J. L.; García, José Luis; Díaz, Eduardo

2009-01-01

Summary: Aromatic compounds belong to one of the most widely distributed classes of organic compounds in nature, and a significant number of xenobiotics belong to this family of compounds. Since many habitats containing large amounts of aromatic compounds are often anoxic, the anaerobic catabolism of aromatic compounds by microorganisms becomes crucial in biogeochemical cycles and in the sustainable development of the biosphere. The mineralization of aromatic compounds by facultative or obligate anaerobic bacteria can be coupled to anaerobic respiration with a variety of electron acceptors as well as to fermentation and anoxygenic photosynthesis. Since the redox potential of the electron-accepting system dictates the degradative strategy, there is wide biochemical diversity among anaerobic aromatic degraders. However, the genetic determinants of all these processes and the mechanisms involved in their regulation are much less studied. This review focuses on the recent findings that standard molecular biology approaches together with new high-throughput technologies (e.g., genome sequencing, transcriptomics, proteomics, and metagenomics) have provided regarding the genetics, regulation, ecophysiology, and evolution of anaerobic aromatic degradation pathways. These studies revealed that the anaerobic catabolism of aromatic compounds is more diverse and widespread than previously thought, and the complex metabolic and stress programs associated with the use of aromatic compounds under anaerobic conditions are starting to be unraveled. Anaerobic biotransformation processes based on unprecedented enzymes and pathways with novel metabolic capabilities, as well as the design of novel regulatory circuits and catabolic networks of great biotechnological potential in synthetic biology, are now feasible to approach. PMID:19258534

Aspects of a Distinct Cytotoxicity of Selenium Salts and Organic Selenides in Living Cells with Possible Implications for Drug Design.

PubMed

Estevam, Ethiene Castellucci; Witek, Karolina; Faulstich, Lisa; Nasim, Muhammad Jawad; Latacz, Gniewomir; Domínguez-Álvarez, Enrique; Kieć-Kononowicz, Katarzyna; Demasi, Marilene; Handzlik, Jadwiga; Jacob, Claus

2015-07-31

Selenium is traditionally considered as an antioxidant element and selenium compounds are often discussed in the context of chemoprevention and therapy. Recent studies, however, have revealed a rather more colorful and diverse biological action of selenium-based compounds, including the modulation of the intracellular redox homeostasis and an often selective interference with regulatory cellular pathways. Our basic activity and mode of action studies with simple selenium and tellurium salts in different strains of Staphylococcus aureus (MRSA) and Saccharomyces cerevisiae indicate that such compounds are sometimes not particularly toxic on their own, yet enhance the antibacterial potential of known antibiotics, possibly via the bioreductive formation of insoluble elemental deposits. Whilst the selenium and tellurium compounds tested do not necessarily act via the generation of Reactive Oxygen Species (ROS), they seem to interfere with various cellular pathways, including a possible inhibition of the proteasome and hindrance of DNA repair. Here, organic selenides are considerably more active compared to simple salts. The interference of selenium (and tellurium) compounds with multiple targets could provide new avenues for the development of effective antibiotic and anticancer agents which may go well beyond the traditional notion of selenium as a simple antioxidant.

GenoLink: a graph-based querying and browsing system for investigating the function of genes and proteins.

PubMed

Durand, Patrick; Labarre, Laurent; Meil, Alain; Divo, Jean-Louis; Vandenbrouck, Yves; Viari, Alain; Wojcik, Jérôme

2006-01-17

A large variety of biological data can be represented by graphs. These graphs can be constructed from heterogeneous data coming from genomic and post-genomic technologies, but there is still need for tools aiming at exploring and analysing such graphs. This paper describes GenoLink, a software platform for the graphical querying and exploration of graphs. GenoLink provides a generic framework for representing and querying data graphs. This framework provides a graph data structure, a graph query engine, allowing to retrieve sub-graphs from the entire data graph, and several graphical interfaces to express such queries and to further explore their results. A query consists in a graph pattern with constraints attached to the vertices and edges. A query result is the set of all sub-graphs of the entire data graph that are isomorphic to the pattern and satisfy the constraints. The graph data structure does not rely upon any particular data model but can dynamically accommodate for any user-supplied data model. However, for genomic and post-genomic applications, we provide a default data model and several parsers for the most popular data sources. GenoLink does not require any programming skill since all operations on graphs and the analysis of the results can be carried out graphically through several dedicated graphical interfaces. GenoLink is a generic and interactive tool allowing biologists to graphically explore various sources of information. GenoLink is distributed either as a standalone application or as a component of the Genostar/Iogma platform. Both distributions are free for academic research and teaching purposes and can be requested at academy@genostar.com. A commercial licence form can be obtained for profit company at info@genostar.com. See also http://www.genostar.org.

GenoLink: a graph-based querying and browsing system for investigating the function of genes and proteins

PubMed Central

Durand, Patrick; Labarre, Laurent; Meil, Alain; Divo1, Jean-Louis; Vandenbrouck, Yves; Viari, Alain; Wojcik, Jérôme

2006-01-01

Background A large variety of biological data can be represented by graphs. These graphs can be constructed from heterogeneous data coming from genomic and post-genomic technologies, but there is still need for tools aiming at exploring and analysing such graphs. This paper describes GenoLink, a software platform for the graphical querying and exploration of graphs. Results GenoLink provides a generic framework for representing and querying data graphs. This framework provides a graph data structure, a graph query engine, allowing to retrieve sub-graphs from the entire data graph, and several graphical interfaces to express such queries and to further explore their results. A query consists in a graph pattern with constraints attached to the vertices and edges. A query result is the set of all sub-graphs of the entire data graph that are isomorphic to the pattern and satisfy the constraints. The graph data structure does not rely upon any particular data model but can dynamically accommodate for any user-supplied data model. However, for genomic and post-genomic applications, we provide a default data model and several parsers for the most popular data sources. GenoLink does not require any programming skill since all operations on graphs and the analysis of the results can be carried out graphically through several dedicated graphical interfaces. Conclusion GenoLink is a generic and interactive tool allowing biologists to graphically explore various sources of information. GenoLink is distributed either as a standalone application or as a component of the Genostar/Iogma platform. Both distributions are free for academic research and teaching purposes and can be requested at academy@genostar.com. A commercial licence form can be obtained for profit company at info@genostar.com. See also . PMID:16417636

Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search

PubMed Central

2014-01-01

Background The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism. Results We are developing a model discovery framework that uses a cell-based modeling platform combined with evolutionary search to automatically search for and identify plausible mechanisms for the biological behavior described in PlanformDB. To automate the evolutionary search we developed a way to compare the output of the modeling platform to the morphological descriptions stored in PlanformDB. We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust, cell-based simulation data. These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation, which provides a quantitative metric of similarity between graphs. The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration. We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism, and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB. Conclusion The work presented here, including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository, has made feasible the automated development, training, and validation of computational models using morphology-based data. This work is part of an ongoing project to automate the search process, which will greatly expand our ability to identify, consider, and test biological mechanisms in the field of regenerative biology. PMID:24917489

Whole-brain structural connectivity in dyskinetic cerebral palsy and its association with motor and cognitive function.

PubMed

Ballester-Plané, Júlia; Schmidt, Ruben; Laporta-Hoyos, Olga; Junqué, Carme; Vázquez, Élida; Delgado, Ignacio; Zubiaurre-Elorza, Leire; Macaya, Alfons; Póo, Pilar; Toro, Esther; de Reus, Marcel A; van den Heuvel, Martijn P; Pueyo, Roser

2017-09-01

Dyskinetic cerebral palsy (CP) has long been associated with basal ganglia and thalamus lesions. Recent evidence further points at white matter (WM) damage. This study aims to identify altered WM pathways in dyskinetic CP from a standardized, connectome-based approach, and to assess structure-function relationship in WM pathways for clinical outcomes. Individual connectome maps of 25 subjects with dyskinetic CP and 24 healthy controls were obtained combining a structural parcellation scheme with whole-brain deterministic tractography. Graph theoretical metrics and the network-based statistic were applied to compare groups and to correlate WM state with motor and cognitive performance. Results showed a widespread reduction of WM volume in CP subjects compared to controls and a more localized decrease in degree (number of links per node) and fractional anisotropy (FA), comprising parieto-occipital regions and the hippocampus. However, supramarginal gyrus showed a significantly higher degree. At the network level, CP subjects showed a bilateral pathway with reduced FA, comprising sensorimotor, intraparietal and fronto-parietal connections. Gross and fine motor functions correlated with FA in a pathway comprising the sensorimotor system, but gross motor also correlated with prefrontal, temporal and occipital connections. Intelligence correlated with FA in a network with fronto-striatal and parieto-frontal connections, and visuoperception was related to right occipital connections. These findings demonstrate a disruption in structural brain connectivity in dyskinetic CP, revealing general involvement of posterior brain regions with relative preservation of prefrontal areas. We identified pathways in which WM integrity is related to clinical features, including but not limited to the sensorimotor system. Hum Brain Mapp 38:4594-4612, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Simulating an Infection Growth Model in Certain Healthy Metabolic Pathways of Homo sapiens for Highlighting Their Role in Type I Diabetes mellitus Using Fire-Spread Strategy, Feedbacks and Sensitivities

PubMed Central

Tagore, Somnath; De, Rajat K.

2013-01-01

Disease Systems Biology is an area of life sciences, which is not very well understood to date. Analyzing infections and their spread in healthy metabolite networks can be one of the focussed areas in this regard. We have proposed a theory based on the classical forest fire model for analyzing the path of infection spread in healthy metabolic pathways. The theory suggests that when fire erupts in a forest, it spreads, and the surrounding trees also catch fire. Similarly, when we consider a metabolic network, the infection caused in the metabolites of the network spreads like a fire. We have constructed a simulation model which is used to study the infection caused in the metabolic networks from the start of infection, to spread and ultimately combating it. For implementation, we have used two approaches, first, based on quantitative strategies using ordinary differential equations and second, using graph-theory based properties. Furthermore, we are using certain probabilistic scores to complete this task and for interpreting the harm caused in the network, given by a ‘critical value’ to check whether the infection can be cured or not. We have tested our simulation model on metabolic pathways involved in Type I Diabetes mellitus in Homo sapiens. For validating our results biologically, we have used sensitivity analysis, both local and global, as well as for identifying the role of feedbacks in spreading infection in metabolic pathways. Moreover, information in literature has also been used to validate the results. The metabolic network datasets have been collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG). PMID:24039701

Curriculum-Based Measurement, Program Development, Graphing Performance and Increasing Efficiency.

ERIC Educational Resources Information Center

Deno, Stanley L.; And Others

1987-01-01

Four brief articles look at aspects of curriculum based measurement (CBM) for academically handicapped students including procedures of CBM with examples, different approaches to graphing student performance, and solutions to the problem of making time to measure student progress frequently. (DB)

The relationships between spatial ability, logical thinking, mathematics performance and kinematics graph interpretation skills of 12th grade physics students

NASA Astrophysics Data System (ADS)

Bektasli, Behzat

Graphs have a broad use in science classrooms, especially in physics. In physics, kinematics is probably the topic for which graphs are most widely used. The participants in this study were from two different grade-12 physics classrooms, advanced placement and calculus-based physics. The main purpose of this study was to search for the relationships between student spatial ability, logical thinking, mathematical achievement, and kinematics graphs interpretation skills. The Purdue Spatial Visualization Test, the Middle Grades Integrated Process Skills Test (MIPT), and the Test of Understanding Graphs in Kinematics (TUG-K) were used for quantitative data collection. Classroom observations were made to acquire ideas about classroom environment and instructional techniques. Factor analysis, simple linear correlation, multiple linear regression, and descriptive statistics were used to analyze the quantitative data. Each instrument has two principal components. The selection and calculation of the slope and of the area were the two principal components of TUG-K. MIPT was composed of a component based upon processing text and a second component based upon processing symbolic information. The Purdue Spatial Visualization Test was composed of a component based upon one-step processing and a second component based upon two-step processing of information. Student ability to determine the slope in a kinematics graph was significantly correlated with spatial ability, logical thinking, and mathematics aptitude and achievement. However, student ability to determine the area in a kinematics graph was only significantly correlated with student pre-calculus semester 2 grades. Male students performed significantly better than female students on the slope items of TUG-K. Also, male students performed significantly better than female students on the PSAT mathematics assessment and spatial ability. This study found that students have different levels of spatial ability, logical thinking, and mathematics aptitude and achievement levels. These different levels were related to student learning of kinematics and they need to be considered when kinematics is being taught. It might be easier for students to understand the kinematics graphs if curriculum developers include more activities related to spatial ability and logical thinking.

The Impact of Microcomputer-Based Science Labs on Children's Graphing Skills.

ERIC Educational Resources Information Center

Mokros, Janice R.

Microcomputer-based laboratories (MBL), the use of microcomputers for student-directed data acquisition and analysis, represents a promising new development in science laboratory instruction. This descriptive study determined the impact of MBLs on middle school students' understanding of graphs of distance and velocity. The study was based on the…

Extraction of Graph Information Based on Image Contents and the Use of Ontology

ERIC Educational Resources Information Center

Kanjanawattana, Sarunya; Kimura, Masaomi

2016-01-01

A graph is an effective form of data representation used to summarize complex information. Explicit information such as the relationship between the X- and Y-axes can be easily extracted from a graph by applying human intelligence. However, implicit knowledge such as information obtained from other related concepts in an ontology also resides in…