Learning and Relevance in Information Retrieval: A Study in the Application of Exploration and User Knowledge to Enhance Performance

ERIC Educational Resources Information Center

Hyman, Harvey

2012-01-01

This dissertation examines the impact of exploration and learning upon eDiscovery information retrieval; it is written in three parts. Part I contains foundational concepts and background on the topics of information retrieval and eDiscovery. This part informs the reader about the research frameworks, methodologies, data collection, and…

A knowledge discovery object model API for Java

PubMed Central

Zuyderduyn, Scott D; Jones, Steven JM

2003-01-01

Background Biological data resources have become heterogeneous and derive from multiple sources. This introduces challenges in the management and utilization of this data in software development. Although efforts are underway to create a standard format for the transmission and storage of biological data, this objective has yet to be fully realized. Results This work describes an application programming interface (API) that provides a framework for developing an effective biological knowledge ontology for Java-based software projects. The API provides a robust framework for the data acquisition and management needs of an ontology implementation. In addition, the API contains classes to assist in creating GUIs to represent this data visually. Conclusions The Knowledge Discovery Object Model (KDOM) API is particularly useful for medium to large applications, or for a number of smaller software projects with common characteristics or objectives. KDOM can be coupled effectively with other biologically relevant APIs and classes. Source code, libraries, documentation and examples are available at . PMID:14583100

Missing Links in Genes to Traits: Toward Teaching for an Integrated Framework of Genetics

ERIC Educational Resources Information Center

Pavlova, Iglika V.; Kreher, Scott A.

2013-01-01

Genetics, one of the most influential fields, underlies all of biology and produces discoveries that are in the news daily. However, many students leave introductory biology and genetics courses lacking a coherent framework of knowledge to use in their daily lives. We identify substantial "missing links" in the teaching of foundational…

From the EBM pyramid to the Greek temple: a new conceptual approach to Guidelines as implementation tools in mental health.

PubMed

Salvador-Carulla, L; Lukersmith, S; Sullivan, W

2017-04-01

Guideline methods to develop recommendations dedicate most effort around organising discovery and corroboration knowledge following the evidence-based medicine (EBM) framework. Guidelines typically use a single dimension of information, and generally discard contextual evidence and formal expert knowledge and consumer's experiences in the process. In recognition of the limitations of guidelines in complex cases, complex interventions and systems research, there has been significant effort to develop new tools, guides, resources and structures to use alongside EBM methods of guideline development. In addition to these advances, a new framework based on the philosophy of science is required. Guidelines should be defined as implementation decision support tools for improving the decision-making process in real-world practice and not only as a procedure to optimise the knowledge base of scientific discovery and corroboration. A shift from the model of the EBM pyramid of corroboration of evidence to the use of broader multi-domain perspective graphically depicted as 'Greek temple' could be considered. This model takes into account the different stages of scientific knowledge (discovery, corroboration and implementation), the sources of knowledge relevant to guideline development (experimental, observational, contextual, expert-based and experiential); their underlying inference mechanisms (deduction, induction, abduction, means-end inferences) and a more precise definition of evidence and related terms. The applicability of this broader approach is presented for the development of the Canadian Consensus Guidelines for the Primary Care of People with Developmental Disabilities.

Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System.

PubMed

Chen, Donghua; Zhang, Runtong; Liu, Kecheng; Hou, Lei

2018-06-19

Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future.

Biochemistry

USDA-ARS?s Scientific Manuscript database

Part of the framework for effective control or management of cyst nematodes depends upon the detailed understanding of their biology. This chapter summarizes fundamental knowledge and recent discoveries about the biochemistry of cyst nematodes, particularly areas related to lipids, carbohydrates and...

Distributed data mining on grids: services, tools, and applications.

PubMed

Cannataro, Mario; Congiusta, Antonio; Pugliese, Andrea; Talia, Domenico; Trunfio, Paolo

2004-12-01

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called Knowledge Grid. This paper describes the Knowledge Grid framework and presents the toolset provided by the Knowledge Grid for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the Knowledge Grid tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

PubMed

Chen, Yi-An; Tripathi, Lokesh P; Mizuguchi, Kenji

2016-01-01

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org. © The Author(s) 2016. Published by Oxford University Press.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework

PubMed Central

Chen, Yi-An; Tripathi, Lokesh P.; Mizuguchi, Kenji

2016-01-01

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org PMID:26989145

Entrepreneurship as a legitimate field of knowledge.

PubMed

Sánchez, José C

2011-08-01

Entrepreneurship as a research topic has been approached from disciplines such as economics, sociology or psychology. After justifying its study, we define the domain of the field, highlighting what has currently become its dominant paradigm, the process of the discovery, assessment and exploitation of opportunities. We then describe the main perspectives and offer an integrated conceptual framework that will allow us to legitimize the study of entrepreneurship as a field of knowledge in its own right. We believe that this framework will help researchers to better recognize the relations among the many factors forming part of the study of entrepreneurship. Lastly, we conclude with some brief reflections on the potential value of the framework presented.

Creating a Ten-Year Science and Innovation Framework for the UK: A Perspective Based on US Experience

ERIC Educational Resources Information Center

Crawley, Edward F.; Greenwald, Suzanne B.

2006-01-01

The sustainability of a competitive, national economy depends largely on the ability of companies to deliver innovative knowledge-intensive goods and services to the market. These are the ultimate outputs of a scientific knowledge system. Ideas flow from the critical, identifiable phases of (a) the discovery, (b) the development, (c) the…

Translational research: understanding the continuum from bench to bedside.

PubMed

Drolet, Brian C; Lorenzi, Nancy M

2011-01-01

The process of translating basic scientific discoveries to clinical applications, and ultimately to public health improvements, has emerged as an important, but difficult, objective in biomedical research. The process is best described as a "translation continuum" because various resources and actions are involved in this progression of knowledge, which advances discoveries from the bench to the bedside. The current model of this continuum focuses primarily on translational research, which is merely one component of the overall translation process. This approach is ineffective. A revised model to address the entire continuum would provide a methodology to identify and describe all translational activities (eg, implementation, adoption translational research, etc) as well their place within the continuum. This manuscript reviews and synthesizes the literature to provide an overview of the current terminology and model for translation. A modification of the existing model is proposed to create a framework called the Biomedical Research Translation Continuum, which defines the translation process and describes the progression of knowledge from laboratory to health gains. This framework clarifies translation for readers who have not followed the evolving and complicated models currently described. Authors and researchers may use the continuum to understand and describe their research better as well as the translational activities within a conceptual framework. Additionally, the framework may increase the advancement of knowledge by refining discussions of translation and allowing more precise identification of barriers to progress. Copyright © 2011 Mosby, Inc. All rights reserved.

Cooperative knowledge evolution: a construction-integration approach to knowledge discovery in medicine.

PubMed

Schmalhofer, F J; Tschaitschian, B

1998-11-01

In this paper, we perform a cognitive analysis of knowledge discovery processes. As a result of this analysis, the construction-integration theory is proposed as a general framework for developing cooperative knowledge evolution systems. We thus suggest that for the acquisition of new domain knowledge in medicine, one should first construct pluralistic views on a given topic which may contain inconsistencies as well as redundancies. Only thereafter does this knowledge become consolidated into a situation-specific circumscription and the early inconsistencies become eliminated. As a proof for the viability of such knowledge acquisition processes in medicine, we present the IDEAS system, which can be used for the intelligent documentation of adverse events in clinical studies. This system provides a better documentation of the side-effects of medical drugs. Thereby, knowledge evolution occurs by achieving consistent explanations in increasingly larger contexts (i.e., more cases and more pharmaceutical substrates). Finally, it is shown how prototypes, model-based approaches and cooperative knowledge evolution systems can be distinguished as different classes of knowledge-based systems.

eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems.

PubMed

Savonnet, Marinette; Leclercq, Eric; Naubourg, Pierre

2016-11-01

Biomedical information systems (BIS) require consideration of three types of variability: data variability induced by new high throughput technologies, schema or model variability induced by large scale studies or new fields of research, and knowledge variability resulting from new discoveries. Beyond data heterogeneity, managing variabilities in the context of BIS requires extensible and dynamic integration process. In this paper, we focus on data and schema variabilities and we propose an integration framework based on ontologies, master data, and semantic annotations. The framework addresses issues related to: 1) collaborative work through a dynamic integration process; 2) variability among studies using an annotation mechanism; and 3) quality control over data and semantic annotations. Our approach relies on two levels of knowledge: BIS-related knowledge is modeled using an application ontology coupled with UML models that allow controlling data completeness and consistency, and domain knowledge is described by a domain ontology, which ensures data coherence. A system build with the eClims framework has been implemented and evaluated in the context of a proteomic platform.

Oak Ridge Graph Analytics for Medical Innovation (ORiGAMI)

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

Roberts, Larry W.; Lee, Sangkeun

2016-01-01

In this era of data-driven decisions and discovery where Big Data is producing Bigger Data, data scientists at the Oak Ridge National Laboratory are leveraging unique leadership infrastructure (e.g., Urika XA and Urika GD appliances) to develop scalable algorithms for semantic, logical and statistical reasoning with Big Data (i.e., data stored in databases as well as unstructured data in documents). ORiGAMI is a next-generation knowledge-discovery framework that is: (a) knowledge nurturing (i.e., evolves seamlessly with newer knowledge and data), (b) smart and curious (i.e. using information-foraging and reasoning algorithms to digest content) and (c) synergistic (i.e., interfaces computers with whatmore » they do best to help subject-matter-experts do their best. ORiGAMI has been demonstrated using the National Library of Medicine's SEMANTIC MEDLINE (archive of medical knowledge since 1994).« less

Energy-Water Nexus Knowledge Discovery Framework

NASA Astrophysics Data System (ADS)

Bhaduri, B. L.; Foster, I.; Chandola, V.; Chen, B.; Sanyal, J.; Allen, M.; McManamay, R.

2017-12-01

As demand for energy grows, the energy sector is experiencing increasing competition for water. With increasing population and changing environmental, socioeconomic scenarios, new technology and investment decisions must be made for optimized and sustainable energy-water resource management. This requires novel scientific insights into the complex interdependencies of energy-water infrastructures across multiple space and time scales. An integrated data driven modeling, analysis, and visualization capability is needed to understand, design, and develop efficient local and regional practices for the energy-water infrastructure components that can be guided with strategic (federal) policy decisions to ensure national energy resilience. To meet this need of the energy-water nexus (EWN) community, an Energy-Water Knowledge Discovery Framework (EWN-KDF) is being proposed to accomplish two objectives: Development of a robust data management and geovisual analytics platform that provides access to disparate and distributed physiographic, critical infrastructure, and socioeconomic data, along with emergent ad-hoc sensor data to provide a powerful toolkit of analysis algorithms and compute resources to empower user-guided data analysis and inquiries; and Demonstration of knowledge generation with selected illustrative use cases for the implications of climate variability for coupled land-water-energy systems through the application of state-of-the art data integration, analysis, and synthesis. Oak Ridge National Laboratory (ORNL), in partnership with Argonne National Laboratory (ANL) and researchers affiliated with the Center for International Earth Science Information Partnership (CIESIN) at Columbia University and State University of New York-Buffalo (SUNY), propose to develop this Energy-Water Knowledge Discovery Framework to generate new, critical insights regarding the complex dynamics of the EWN and its interactions with climate variability and change. An overarching objective of this project is to integrate impacts, adaptation, and vulnerability (IAV) science with emerging data science to meet the data analysis needs of the U.S. Department of Energy and partner federal agencies with respect to the EWN.

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

DOE PAGES

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; ...

2013-01-01

Background . The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective . To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods . The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expertmore » knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results . The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions . Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

PubMed Central

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Varnum, Susan M.; Brown, Joseph N.; Riensche, Roderick M.; Adkins, Joshua N.; Jacobs, Jon M.; Hoidal, John R.; Scholand, Mary Beth; Pounds, Joel G.; Blackburn, Michael R.; Rodland, Karin D.; McDermott, Jason E.

2013-01-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification. PMID:24223463

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

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

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.

2013-10-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integratedmore » into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less

A Semantic Lexicon-Based Approach for Sense Disambiguation and Its WWW Application

NASA Astrophysics Data System (ADS)

di Lecce, Vincenzo; Calabrese, Marco; Soldo, Domenico

This work proposes a basic framework for resolving sense disambiguation through the use of Semantic Lexicon, a machine readable dictionary managing both word senses and lexico-semantic relations. More specifically, polysemous ambiguity characterizing Web documents is discussed. The adopted Semantic Lexicon is WordNet, a lexical knowledge-base of English words widely adopted in many research studies referring to knowledge discovery. The proposed approach extends recent works on knowledge discovery by focusing on the sense disambiguation aspect. By exploiting the structure of WordNet database, lexico-semantic features are used to resolve the inherent sense ambiguity of written text with particular reference to HTML resources. The obtained results may be extended to generic hypertextual repositories as well. Experiments show that polysemy reduction can be used to hint about the meaning of specific senses in given contexts.

Epigenetics: An Emerging Framework for Advanced Practice Psychiatric Nursing.

PubMed

DeSocio, Janiece E

2016-07-01

The aims of this paper are to synthesize and report research findings from neuroscience and epigenetics that contribute to an emerging explanatory framework for advanced practice psychiatric nursing. Discoveries in neuroscience and epigenetics reveal synergistic mechanisms that support the integration of psychotherapy, psychopharmacology, and psychoeducation in practice. Advanced practice psychiatric nurses will benefit from an expanded knowledge base in neuroscience and epigenetics that informs and explains the scientific rationale for our integrated practice. © 2015 Wiley Periodicals, Inc.

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics

PubMed Central

2017-01-01

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration. PMID:29065473

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics.

PubMed

Sotelo Monge, Marco Antonio; Maestre Vidal, Jorge; García Villalba, Luis Javier

2017-10-21

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration.

A framework for interval-valued information system

NASA Astrophysics Data System (ADS)

Yin, Yunfei; Gong, Guanghong; Han, Liang

2012-09-01

Interval-valued information system is used to transform the conventional dataset into the interval-valued form. To conduct the interval-valued data mining, we conduct two investigations: (1) construct the interval-valued information system, and (2) conduct the interval-valued knowledge discovery. In constructing the interval-valued information system, we first make the paired attributes in the database discovered, and then, make them stored in the neighbour locations in a common database and regard them as 'one' new field. In conducting the interval-valued knowledge discovery, we utilise some related priori knowledge and regard the priori knowledge as the control objectives; and design an approximate closed-loop control mining system. On the implemented experimental platform (prototype), we conduct the corresponding experiments and compare the proposed algorithms with several typical algorithms, such as the Apriori algorithm, the FP-growth algorithm and the CLOSE+ algorithm. The experimental results show that the interval-valued information system method is more effective than the conventional algorithms in discovering interval-valued patterns.

A Framework for Information Retrieval and Knowledge Discovery from Online Healthcare Forums

ERIC Educational Resources Information Center

Sampathkumar, Hariprasad

2016-01-01

Information used to assist biomedical and clinical research has largely comprised of data available in published sources like scientific papers and journals, or in clinical sources like patient health records, lab reports and discharge summaries. Information from such sources, though extensive and organized, is often not readily available due to…

Panacea, a semantic-enabled drug recommendations discovery framework.

PubMed

Doulaverakis, Charalampos; Nikolaidis, George; Kleontas, Athanasios; Kompatsiaris, Ioannis

2014-03-06

Personalized drug prescription can be benefited from the use of intelligent information management and sharing. International standard classifications and terminologies have been developed in order to provide unique and unambiguous information representation. Such standards can be used as the basis of automated decision support systems for providing drug-drug and drug-disease interaction discovery. Additionally, Semantic Web technologies have been proposed in earlier works, in order to support such systems. The paper presents Panacea, a semantic framework capable of offering drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standard classifications and terminologies, provide the backbone of the common representation of medical data while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Representation is based on a lightweight ontology. A layered reasoning approach is implemented where at the first layer ontological inference is used in order to discover underlying knowledge, while at the second layer a two-step rule selection strategy is followed resulting in a computationally efficient reasoning approach. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. Panacea is evaluated both in terms of quality of recommendations against real clinical data and performance. The quality recommendation gave useful insights regarding requirements for real world deployment and revealed several parameters that affected the recommendation results. Performance-wise, Panacea is compared to a previous published work by the authors, a service for drug recommendations named GalenOWL, and presents their differences in modeling and approach to the problem, while also pinpointing the advantages of Panacea. Overall, the paper presents a framework for providing an efficient drug recommendations service where Semantic Web technologies are coupled with traditional business rule engines.

Using key performance indicators as knowledge-management tools at a regional health-care authority level.

PubMed

Berler, Alexander; Pavlopoulos, Sotiris; Koutsouris, Dimitris

2005-06-01

The advantages of the introduction of information and communication technologies in the complex health-care sector are already well-known and well-stated in the past. It is, nevertheless, paradoxical that although the medical community has embraced with satisfaction most of the technological discoveries allowing the improvement in patient care, this has not happened when talking about health-care informatics. Taking the above issue of concern, our work proposes an information model for knowledge management (KM) based upon the use of key performance indicators (KPIs) in health-care systems. Based upon the use of the balanced scorecard (BSC) framework (Kaplan/Norton) and quality assurance techniques in health care (Donabedian), this paper is proposing a patient journey centered approach that drives information flow at all levels of the day-to-day process of delivering effective and managed care, toward information assessment and knowledge discovery. In order to persuade health-care decision-makers to assess the added value of KM tools, those should be used to propose new performance measurement and performance management techniques at all levels of a health-care system. The proposed KPIs are forming a complete set of metrics that enable the performance management of a regional health-care system. In addition, the performance framework established is technically applied by the use of state-of-the-art KM tools such as data warehouses and business intelligence information systems. In that sense, the proposed infrastructure is, technologically speaking, an important KM tool that enables knowledge sharing amongst various health-care stakeholders and between different health-care groups. The use of BSC is an enabling framework toward a KM strategy in health care.

The Service Environment for Enhanced Knowledge and Research (SEEKR) Framework

NASA Astrophysics Data System (ADS)

King, T. A.; Walker, R. J.; Weigel, R. S.; Narock, T. W.; McGuire, R. E.; Candey, R. M.

2011-12-01

The Service Environment for Enhanced Knowledge and Research (SEEKR) Framework is a configurable service oriented framework to enable the discovery, access and analysis of data shared in a community. The SEEKR framework integrates many existing independent services through the use of web technologies and standard metadata. Services are hosted on systems by using an application server and are callable by using REpresentational State Transfer (REST) protocols. Messages and metadata are transferred with eXtensible Markup Language (XML) encoding which conform to a published XML schema. Space Physics Archive Search and Extract (SPASE) metadata is central to utilizing the services. Resources (data, documents, software, etc.) are described with SPASE and the associated Resource Identifier is used to access and exchange resources. The configurable options for the service can be set by using a web interface. Services are packaged as web application resource (WAR) files for direct deployment on application services such as Tomcat or Jetty. We discuss the composition of the SEEKR framework, how new services can be integrated and the steps necessary to deploying the framework. The SEEKR Framework emerged from NASA's Virtual Magnetospheric Observatory (VMO) and other systems and we present an overview of these systems from a SEEKR Framework perspective.

Handling knowledge via Concept Maps: a space weather use case

NASA Astrophysics Data System (ADS)

Messerotti, Mauro; Fox, Peter

Concept Maps (Cmaps) are powerful means for knowledge coding in graphical form. As flexible software tools exist to manipulate the knowledge embedded in Cmaps in machine-readable form, such complex entities are suitable candidates not only for the representation of ontologies and semantics in Virtual Observatory (VO) architectures, but also for knowledge handling and knowledge discovery. In this work, we present a use case relevant to space weather applications and we elaborate on its possible implementation and adavanced use in Semantic Virtual Observatories dedicated to Sun-Earth Connections. This analysis was carried out in the framework of the Electronic Geophysical Year (eGY) and represents an achievement synergized by the eGY Virtual Observatories Working Group.

Knowledge for Healthcare: the future of health librarianship.

PubMed

Bryant, Sue Lacey; Stewart, David; Goswami, Louise

2015-09-01

Many people are still not receiving the right care. It is imperative for health care librarians to come together around a common vision to achieve Knowledge for Healthcare so that the right knowledge and evidence is used at the right time in the right place. The authors describe five workstreams within a modernisation programme: Service Transformation, Workforce Planning and Development, Quality and Impact, Resource Discovery and Optimising Investment. Communications, engagement and partnership working are central to success. The development framework sets out principles on which to base decisions, and design criteria for transforming services. © 2015 Health Libraries Group.

Systematic identification of latent disease-gene associations from PubMed articles.

PubMed

Zhang, Yuji; Shen, Feichen; Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

Systematic identification of latent disease-gene associations from PubMed articles

PubMed Central

Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research. PMID:29373609

A bayesian translational framework for knowledge propagation, discovery, and integration under specific contexts.

PubMed

Deng, Michelle; Zollanvari, Amin; Alterovitz, Gil

2012-01-01

The immense corpus of biomedical literature existing today poses challenges in information search and integration. Many links between pieces of knowledge occur or are significant only under certain contexts-rather than under the entire corpus. This study proposes using networks of ontology concepts, linked based on their co-occurrences in annotations of abstracts of biomedical literature and descriptions of experiments, to draw conclusions based on context-specific queries and to better integrate existing knowledge. In particular, a Bayesian network framework is constructed to allow for the linking of related terms from two biomedical ontologies under the queried context concept. Edges in such a Bayesian network allow associations between biomedical concepts to be quantified and inference to be made about the existence of some concepts given prior information about others. This approach could potentially be a powerful inferential tool for context-specific queries, applicable to ontologies in other fields as well.

A Bayesian Translational Framework for Knowledge Propagation, Discovery, and Integration Under Specific Contexts

PubMed Central

Deng, Michelle; Zollanvari, Amin; Alterovitz, Gil

2012-01-01

The immense corpus of biomedical literature existing today poses challenges in information search and integration. Many links between pieces of knowledge occur or are significant only under certain contexts—rather than under the entire corpus. This study proposes using networks of ontology concepts, linked based on their co-occurrences in annotations of abstracts of biomedical literature and descriptions of experiments, to draw conclusions based on context-specific queries and to better integrate existing knowledge. In particular, a Bayesian network framework is constructed to allow for the linking of related terms from two biomedical ontologies under the queried context concept. Edges in such a Bayesian network allow associations between biomedical concepts to be quantified and inference to be made about the existence of some concepts given prior information about others. This approach could potentially be a powerful inferential tool for context-specific queries, applicable to ontologies in other fields as well. PMID:22779044

Environmental Visualization and Horizontal Fusion

DTIC Science & Technology

2005-10-01

the section on EVIS Rules. Federated Search – Discovering Content Another method of discovering services and their content has been implemented...in HF through a next-generation knowledge discovery framework called Federated Search . A virtual information space, called Collateral Space was...environmental mission effects products, is presented later in the paper. Federated Search allows users to search through Collateral Space data that is

An Approach for Automatic Generation of Adaptive Hypermedia in Education with Multilingual Knowledge Discovery Techniques

ERIC Educational Resources Information Center

Alfonseca, Enrique; Rodriguez, Pilar; Perez, Diana

2007-01-01

This work describes a framework that combines techniques from Adaptive Hypermedia and Natural Language processing in order to create, in a fully automated way, on-line information systems from linear texts in electronic format, such as textbooks. The process is divided into two steps: an "off-line" processing step, which analyses the source text,…

Transforming practice into clinical scholarship.

PubMed

Limoges, Jacqueline; Acorn, Sonia

2016-04-01

The aims of this paper were to explicate clinical scholarship as synonymous with the scholarship of application and to explore the evolution of scholarly practice to clinical scholarship. Boyer contributed an expanded view of scholarship that recognized various approaches to knowledge production beyond pure research (discovery) to include the scholarship of integration, application and teaching. There is growing interest in using Boyer's framework to advance knowledge production in nursing but the discussion of clinical scholarship in relation to Boyer's framework is sparse. Discussion paper. Literature from 1983-2015 and Boyer's framework. When clinical scholarship is viewed as a synonym for Boyer's scholarship of application, it can be aligned to this well established framework to support knowledge generated in clinical practice. For instance, applying the three criteria for scholarship (documentation, peer review and dissemination) can ensure that the knowledge produced is rigorous, available for critique and used by others to advance nursing practice and patient care. Understanding the differences between scholarly practice and clinical scholarship can promote the development of clinical scholarship. Supporting clinical leaders to identify issues confronting nursing practice can enable scholarly practice to be transformed into clinical scholarship. Expanding the understanding of clinical scholarship and linking it to Boyer's scholarship of application can assist nurses to generate knowledge that addresses clinical concerns. Further dialogue about how clinical scholarship can address the theory-practice gap and how publication of clinical scholarship could be expanded given the goals of clinical scholarship is warranted. © 2016 John Wiley & Sons Ltd.

Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge

PubMed Central

2014-01-01

Background Combining different sources of knowledge to build improved structure activity relationship models is not easy owing to the variety of knowledge formats and the absence of a common framework to interoperate between learning techniques. Most of the current approaches address this problem by using consensus models that operate at the prediction level. We explore the possibility to directly combine these sources at the knowledge level, with the aim to harvest potentially increased synergy at an earlier stage. Our goal is to design a general methodology to facilitate knowledge discovery and produce accurate and interpretable models. Results To combine models at the knowledge level, we propose to decouple the learning phase from the knowledge application phase using a pivot representation (lingua franca) based on the concept of hypothesis. A hypothesis is a simple and interpretable knowledge unit. Regardless of its origin, knowledge is broken down into a collection of hypotheses. These hypotheses are subsequently organised into hierarchical network. This unification permits to combine different sources of knowledge into a common formalised framework. The approach allows us to create a synergistic system between different forms of knowledge and new algorithms can be applied to leverage this unified model. This first article focuses on the general principle of the Self Organising Hypothesis Network (SOHN) approach in the context of binary classification problems along with an illustrative application to the prediction of mutagenicity. Conclusion It is possible to represent knowledge in the unified form of a hypothesis network allowing interpretable predictions with performances comparable to mainstream machine learning techniques. This new approach offers the potential to combine knowledge from different sources into a common framework in which high level reasoning and meta-learning can be applied; these latter perspectives will be explored in future work. PMID:24959206

Working with Data: Discovering Knowledge through Mining and Analysis; Systematic Knowledge Management and Knowledge Discovery; Text Mining; Methodological Approach in Discovering User Search Patterns through Web Log Analysis; Knowledge Discovery in Databases Using Formal Concept Analysis; Knowledge Discovery with a Little Perspective.

ERIC Educational Resources Information Center

Qin, Jian; Jurisica, Igor; Liddy, Elizabeth D.; Jansen, Bernard J; Spink, Amanda; Priss, Uta; Norton, Melanie J.

2000-01-01

These six articles discuss knowledge discovery in databases (KDD). Topics include data mining; knowledge management systems; applications of knowledge discovery; text and Web mining; text mining and information retrieval; user search patterns through Web log analysis; concept analysis; data collection; and data structure inconsistency. (LRW)

ESIP's Earth Science Knowledge Graph (ESKG) Testbed Project: An Automatic Approach to Building Interdisciplinary Earth Science Knowledge Graphs to Improve Data Discovery

NASA Astrophysics Data System (ADS)

McGibbney, L. J.; Jiang, Y.; Burgess, A. B.

2017-12-01

Big Earth observation data have been produced, archived and made available online, but discovering the right data in a manner that precisely and efficiently satisfies user needs presents a significant challenge to the Earth Science (ES) community. An emerging trend in information retrieval community is to utilize knowledge graphs to assist users in quickly finding desired information from across knowledge sources. This is particularly prevalent within the fields of social media and complex multimodal information processing to name but a few, however building a domain-specific knowledge graph is labour-intensive and hard to keep up-to-date. In this work, we update our progress on the Earth Science Knowledge Graph (ESKG) project; an ESIP-funded testbed project which provides an automatic approach to building a dynamic knowledge graph for ES to improve interdisciplinary data discovery by leveraging implicit, latent existing knowledge present within across several U.S Federal Agencies e.g. NASA, NOAA and USGS. ESKG strengthens ties between observations and user communities by: 1) developing a knowledge graph derived from various sources e.g. Web pages, Web Services, etc. via natural language processing and knowledge extraction techniques; 2) allowing users to traverse, explore, query, reason and navigate ES data via knowledge graph interaction. ESKG has the potential to revolutionize the way in which ES communities interact with ES data in the open world through the entity, spatial and temporal linkages and characteristics that make it up. This project enables the advancement of ESIP collaboration areas including both Discovery and Semantic Technologies by putting graph information right at our fingertips in an interactive, modern manner and reducing the efforts to constructing ontology. To demonstrate the ESKG concept, we will demonstrate use of our framework across NASA JPL's PO.DAAC, NOAA's Earth Observation Requirements Evaluation System (EORES) and various USGS systems.

EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos.

PubMed

Schott, Benjamin; Traub, Manuel; Schlagenhauf, Cornelia; Takamiya, Masanari; Antritter, Thomas; Bartschat, Andreas; Löffler, Katharina; Blessing, Denis; Otte, Jens C; Kobitski, Andrei Y; Nienhaus, G Ulrich; Strähle, Uwe; Mikut, Ralf; Stegmaier, Johannes

2018-04-01

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055

An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System.

PubMed

Wu, Zhenyu; Xu, Yuan; Yang, Yunong; Zhang, Chunhong; Zhu, Xinning; Ji, Yang

2017-02-20

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.

Cancer biology and implications for practice.

PubMed

Rieger, Paula Trahan

2006-08-01

The media seem to announce a new scientific discovery related to cancer daily. Oncology nurses are challenged to keep up with the explosion of new knowledge and to understand how it ultimately relates to the care of patients with cancer. A framework for classifying new knowledge can be useful as nurses seek to understand the biology of cancer and its related implications for practice. To understand the molecular roots of cancer, healthcare practitioners specializing in cancer care require insight into genes, their messages, and the proteins produced from those messages, as well as the new tools of molecular biology.

A Methodology for the Analysis of Programmer Productivity and Effort Estimation within the Framework of Software Conversion.

DTIC Science & Technology

1984-05-01

TEST CH~ART NAT ONAL BQREAu Or s T AADS-963-’ 82 The coefficient of KCA(programmer’s knowledge of the program) initially seemed to be an error...productivity, as expected. An interesting manifestation, supporting a discovery by Oliver, was exhibited by the rating of a programmer’s knowledge of...ACCESSION NO. 3. RECIPIENT’S CATALOG NUM13ER 10 AFIT/CI/NR 84-44D _ _ ___)___________ 4. TITLE (.,d S ..benli) PR#fQP6rV/rV S . TYPE OF REPORT A PERIOD

Energy-Water Nexus Knowledge Discovery Framework, Experts’ Meeting Report

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

Bhaduri, Budhendra L.; Simon, AJ; Allen, Melissa R.

Energy and water generation and delivery systems are inherently interconnected. With worldwide demandfor energy growing, the energy sector is experiencing increasing competition for water. With increasingpopulation and changing environmental, socioeconomic, and demographic scenarios, new technology andinvestment decisions must be made for optimized and sustainable energy-water resource management. These decisions require novel scientific insights into the complex interdependencies of energy-water infrastructures across multiple space and time scales.

Toward Routine Automatic Pathway Discovery from On-line Scientific Text Abstracts.

PubMed

Ng; Wong

1999-01-01

We are entering a new era of research where the latest scientific discoveries are often first reported online and are readily accessible by scientists worldwide. This rapid electronic dissemination of research breakthroughs has greatly accelerated the current pace in genomics and proteomics research. The race to the discovery of a gene or a drug has now become increasingly dependent on how quickly a scientist can scan through voluminous amount of information available online to construct the relevant picture (such as protein-protein interaction pathways) as it takes shape amongst the rapidly expanding pool of globally accessible biological data (e.g. GENBANK) and scientific literature (e.g. MEDLINE). We describe a prototype system for automatic pathway discovery from on-line text abstracts, combining technologies that (1) retrieve research abstracts from online sources, (2) extract relevant information from the free texts, and (3) present the extracted information graphically and intuitively. Our work demonstrates that this framework allows us to routinely scan online scientific literature for automatic discovery of knowledge, giving modern scientists the necessary competitive edge in managing the information explosion in this electronic age.

Computer-aided discovery of a metal-organic framework with superior oxygen uptake.

PubMed

Moghadam, Peyman Z; Islamoglu, Timur; Goswami, Subhadip; Exley, Jason; Fantham, Marcus; Kaminski, Clemens F; Snurr, Randall Q; Farha, Omar K; Fairen-Jimenez, David

2018-04-11

Current advances in materials science have resulted in the rapid emergence of thousands of functional adsorbent materials in recent years. This clearly creates multiple opportunities for their potential application, but it also creates the following challenge: how does one identify the most promising structures, among the thousands of possibilities, for a particular application? Here, we present a case of computer-aided material discovery, in which we complete the full cycle from computational screening of metal-organic framework materials for oxygen storage, to identification, synthesis and measurement of oxygen adsorption in the top-ranked structure. We introduce an interactive visualization concept to analyze over 1000 unique structure-property plots in five dimensions and delimit the relationships between structural properties and oxygen adsorption performance at different pressures for 2932 already-synthesized structures. We also report a world-record holding material for oxygen storage, UMCM-152, which delivers 22.5% more oxygen than the best known material to date, to the best of our knowledge.

A deep learning and novelty detection framework for rapid phenotyping in high-content screening

PubMed Central

Sommer, Christoph; Hoefler, Rudolf; Samwer, Matthias; Gerlich, Daniel W.

2017-01-01

Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening. PMID:28954863

A unified framework for managing provenance information in translational research

PubMed Central

2011-01-01

Background A critical aspect of the NIH Translational Research roadmap, which seeks to accelerate the delivery of "bench-side" discoveries to patient's "bedside," is the management of the provenance metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate "domain semantics", which is essential to support domain-specific querying and analysis by scientists. Results We identify a common set of challenges in managing provenance information across the pre-publication and post-publication phases of data in the translational research lifecycle. We define the semantic provenance framework (SPF), underpinned by the Provenir upper-level provenance ontology, to address these challenges in the four stages of provenance metadata: (a) Provenance collection - during data generation (b) Provenance representation - to support interoperability, reasoning, and incorporate domain semantics (c) Provenance storage and propagation - to allow efficient storage and seamless propagation of provenance as the data is transferred across applications (d) Provenance query - to support queries with increasing complexity over large data size and also support knowledge discovery applications We apply the SPF to two exemplar translational research projects, namely the Semantic Problem Solving Environment for Trypanosoma cruzi (T.cruzi SPSE) and the Biomedical Knowledge Repository (BKR) project, to demonstrate its effectiveness. Conclusions The SPF provides a unified framework to effectively manage provenance of translational research data during pre and post-publication phases. This framework is underpinned by an upper-level provenance ontology called Provenir that is extended to create domain-specific provenance ontologies to facilitate provenance interoperability, seamless propagation of provenance, automated querying, and analysis. PMID:22126369

Leveraging ecological theory to guide natural product discovery.

PubMed

Smanski, Michael J; Schlatter, Daniel C; Kinkel, Linda L

2016-03-01

Technological improvements have accelerated natural product (NP) discovery and engineering to the point that systematic genome mining for new molecules is on the horizon. NP biosynthetic potential is not equally distributed across organisms, environments, or microbial life histories, but instead is enriched in a number of prolific clades. Also, NPs are not equally abundant in nature; some are quite common and others markedly rare. Armed with this knowledge, random 'fishing expeditions' for new NPs are increasingly harder to justify. Understanding the ecological and evolutionary pressures that drive the non-uniform distribution of NP biosynthesis provides a rational framework for the targeted isolation of strains enriched in new NP potential. Additionally, ecological theory leads to testable hypotheses regarding the roles of NPs in shaping ecosystems. Here we review several recent strain prioritization practices and discuss the ecological and evolutionary underpinnings for each. Finally, we offer perspectives on leveraging microbial ecology and evolutionary biology for future NP discovery.

KOJAK: Scalable Semantic Link Discovery Via Integrated Knowledge-Based and Statistical Reasoning

DTIC Science & Technology

2006-11-01

program can find interesting connections in a network without having to learn the patterns of interestingness beforehand. The key advantage of our...Interesting Instances in Semantic Graphs Below we describe how the UNICORN framework can discover interesting instances in a multi-relational dataset...We can now describe how UNICORN solves the first problem of finding the top interesting nodes in a semantic net by ranking them according to

A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.

PubMed

Lara, Juan A; Lizcano, David; Pérez, Aurora; Valente, Juan P

2014-10-01

There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG). Copyright © 2014 Elsevier Inc. All rights reserved.

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System

PubMed Central

Wu, Zhenyu; Xu, Yuan; Yang, Yunong; Zhang, Chunhong; Zhu, Xinning; Ji, Yang

2017-01-01

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed. PMID:28230725

System Architecture Development for Energy and Water Infrastructure Data Management and Geovisual Analytics

NASA Astrophysics Data System (ADS)

Berres, A.; Karthik, R.; Nugent, P.; Sorokine, A.; Myers, A.; Pang, H.

2017-12-01

Building an integrated data infrastructure that can meet the needs of a sustainable energy-water resource management requires a robust data management and geovisual analytics platform, capable of cross-domain scientific discovery and knowledge generation. Such a platform can facilitate the investigation of diverse complex research and policy questions for emerging priorities in Energy-Water Nexus (EWN) science areas. Using advanced data analytics, machine learning techniques, multi-dimensional statistical tools, and interactive geovisualization components, such a multi-layered federated platform is being developed, the Energy-Water Nexus Knowledge Discovery Framework (EWN-KDF). This platform utilizes several enterprise-grade software design concepts and standards such as extensible service-oriented architecture, open standard protocols, event-driven programming model, enterprise service bus, and adaptive user interfaces to provide a strategic value to the integrative computational and data infrastructure. EWN-KDF is built on the Compute and Data Environment for Science (CADES) environment in Oak Ridge National Laboratory (ORNL).

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

PubMed Central

Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S.; Niu, Xufeng; Zhang, Jinfeng

2011-01-01

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. PMID:21738677

Leveraging Semantic Labels for Multi-level Abstraction in Medical Process Mining and Trace Comparison.

PubMed

Leonardi, Giorgio; Striani, Manuel; Quaglini, Silvana; Cavallini, Anna; Montani, Stefania

2018-05-21

Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept. Trace comparison resorts to a similarity metric able to take into account abstraction phase penalties, and to deal with quantitative and qualitative temporal constraints in abstracted traces. As for process discovery, we rely on classical algorithms embedded in the framework ProM, made semantic by the capability of abstracting the actions on the basis of their conceptual meaning. The approach has been tested in stroke care, where we adopted abstraction and trace comparison to cluster event logs of different stroke units, to highlight (in)correct behavior, abstracting from details. We also provide process discovery results, showing how the abstraction mechanism allows to obtain stroke process models more easily interpretable by neurologists. Copyright © 2018. Published by Elsevier Inc.

OWL reasoning framework over big biological knowledge network.

PubMed

Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

2014-01-01

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

OWL Reasoning Framework over Big Biological Knowledge Network

PubMed Central

Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

2014-01-01

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity. PMID:24877076

Effective knowledge management in translational medicine.

PubMed

Szalma, Sándor; Koka, Venkata; Khasanova, Tatiana; Perakslis, Eric D

2010-07-19

The growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health. The pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern. The system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface. The implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs.

An automated framework for hypotheses generation using literature.

PubMed

Abedi, Vida; Zand, Ramin; Yeasin, Mohammed; Faisal, Fazle Elahi

2012-08-29

In bio-medicine, exploratory studies and hypothesis generation often begin with researching existing literature to identify a set of factors and their association with diseases, phenotypes, or biological processes. Many scientists are overwhelmed by the sheer volume of literature on a disease when they plan to generate a new hypothesis or study a biological phenomenon. The situation is even worse for junior investigators who often find it difficult to formulate new hypotheses or, more importantly, corroborate if their hypothesis is consistent with existing literature. It is a daunting task to be abreast with so much being published and also remember all combinations of direct and indirect associations. Fortunately there is a growing trend of using literature mining and knowledge discovery tools in biomedical research. However, there is still a large gap between the huge amount of effort and resources invested in disease research and the little effort in harvesting the published knowledge. The proposed hypothesis generation framework (HGF) finds "crisp semantic associations" among entities of interest - that is a step towards bridging such gaps. The proposed HGF shares similar end goals like the SWAN but are more holistic in nature and was designed and implemented using scalable and efficient computational models of disease-disease interaction. The integration of mapping ontologies with latent semantic analysis is critical in capturing domain specific direct and indirect "crisp" associations, and making assertions about entities (such as disease X is associated with a set of factors Z). Pilot studies were performed using two diseases. A comparative analysis of the computed "associations" and "assertions" with curated expert knowledge was performed to validate the results. It was observed that the HGF is able to capture "crisp" direct and indirect associations, and provide knowledge discovery on demand. The proposed framework is fast, efficient, and robust in generating new hypotheses to identify factors associated with a disease. A full integrated Web service application is being developed for wide dissemination of the HGF. A large-scale study by the domain experts and associated researchers is underway to validate the associations and assertions computed by the HGF.

Ethical, legal, and social issues in the translation of genomics into health care.

PubMed

Badzek, Laurie; Henaghan, Mark; Turner, Martha; Monsen, Rita

2013-03-01

The rapid continuous feed of new information from scientific discoveries related to the human genome makes translation and incorporation of information into the clinical setting difficult and creates ethical, legal, and social challenges for providers. This article overviews some of the legal and ethical foundations that guide our response to current complex issues in health care associated with the impact of scientific discoveries related to the human genome. Overlapping ethical, legal, and social implications impact nurses and other healthcare professionals as they seek to identify and translate into practice important information related to new genomic scientific knowledge. Ethical and legal foundations such as professional codes, human dignity, and human rights provide the framework for understanding highly complex genomic issues. Ethical, legal, and social concerns of the health provider in the translation of genomic knowledge into practice including minimizing harms, maximizing benefits, transparency, confidentiality, and informed consent are described. Additionally, nursing professional competencies related to ethical, legal, and social issues in the translation of genomics into health care are discussed. Ethical, legal, and social considerations in new genomic discovery necessitate that healthcare professionals have knowledge and competence to respond to complex genomic issues and provide appropriate information and care to patients, families, and communities. Understanding the ethical, legal, and social issues in the translation of genomic information into practice is essential to provide patients, families, and communities with competent, safe, effective health care. © 2013 Sigma Theta Tau International.

Semantic biomedical resource discovery: a Natural Language Processing framework.

PubMed

Sfakianaki, Pepi; Koumakis, Lefteris; Sfakianakis, Stelios; Iatraki, Galatia; Zacharioudakis, Giorgos; Graf, Norbert; Marias, Kostas; Tsiknakis, Manolis

2015-09-30

A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. A Natural Language Processing engine which can "translate" free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ). For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.

MachineProse: an Ontological Framework for Scientific Assertions

PubMed Central

Dinakarpandian, Deendayal; Lee, Yugyung; Vishwanath, Kartik; Lingambhotla, Rohini

2006-01-01

Objective: The idea of testing a hypothesis is central to the practice of biomedical research. However, the results of testing a hypothesis are published mainly in the form of prose articles. Encoding the results as scientific assertions that are both human and machine readable would greatly enhance the synergistic growth and dissemination of knowledge. Design: We have developed MachineProse (MP), an ontological framework for the concise specification of scientific assertions. MP is based on the idea of an assertion constituting a fundamental unit of knowledge. This is in contrast to current approaches that use discrete concept terms from domain ontologies for annotation and assertions are only inferred heuristically. Measurements: We use illustrative examples to highlight the advantages of MP over the use of the Medical Subject Headings (MeSH) system and keywords in indexing scientific articles. Results: We show how MP makes it possible to carry out semantic annotation of publications that is machine readable and allows for precise search capabilities. In addition, when used by itself, MP serves as a knowledge repository for emerging discoveries. A prototype for proof of concept has been developed that demonstrates the feasibility and novel benefits of MP. As part of the MP framework, we have created an ontology of relationship types with about 100 terms optimized for the representation of scientific assertions. Conclusion: MachineProse is a novel semantic framework that we believe may be used to summarize research findings, annotate biomedical publications, and support sophisticated searches. PMID:16357355

Knowledge Discovery from Databases: An Introductory Review.

ERIC Educational Resources Information Center

Vickery, Brian

1997-01-01

Introduces new procedures being used to extract knowledge from databases and discusses rationales for developing knowledge discovery methods. Methods are described for such techniques as classification, clustering, and the detection of deviations from pre-established norms. Examines potential uses of knowledge discovery in the information field.…

Global Health Innovation Technology Models.

PubMed

Harding, Kimberly

2016-01-01

Chronic technology and business process disparities between High Income, Low Middle Income and Low Income (HIC, LMIC, LIC) research collaborators directly prevent the growth of sustainable Global Health innovation for infectious and rare diseases. There is a need for an Open Source-Open Science Architecture Framework to bridge this divide. We are proposing such a framework for consideration by the Global Health community, by utilizing a hybrid approach of integrating agnostic Open Source technology and healthcare interoperability standards and Total Quality Management principles. We will validate this architecture framework through our programme called Project Orchid. Project Orchid is a conceptual Clinical Intelligence Exchange and Virtual Innovation platform utilizing this approach to support clinical innovation efforts for multi-national collaboration that can be locally sustainable for LIC and LMIC research cohorts. The goal is to enable LIC and LMIC research organizations to accelerate their clinical trial process maturity in the field of drug discovery, population health innovation initiatives and public domain knowledge networks. When sponsored, this concept will be tested by 12 confirmed clinical research and public health organizations in six countries. The potential impact of this platform is reduced drug discovery and public health innovation lag time and improved clinical trial interventions, due to reliable clinical intelligence and bio-surveillance across all phases of the clinical innovation process.

Global Health Innovation Technology Models

PubMed Central

Harding, Kimberly

2016-01-01

Chronic technology and business process disparities between High Income, Low Middle Income and Low Income (HIC, LMIC, LIC) research collaborators directly prevent the growth of sustainable Global Health innovation for infectious and rare diseases. There is a need for an Open Source-Open Science Architecture Framework to bridge this divide. We are proposing such a framework for consideration by the Global Health community, by utilizing a hybrid approach of integrating agnostic Open Source technology and healthcare interoperability standards and Total Quality Management principles. We will validate this architecture framework through our programme called Project Orchid. Project Orchid is a conceptual Clinical Intelligence Exchange and Virtual Innovation platform utilizing this approach to support clinical innovation efforts for multi-national collaboration that can be locally sustainable for LIC and LMIC research cohorts. The goal is to enable LIC and LMIC research organizations to accelerate their clinical trial process maturity in the field of drug discovery, population health innovation initiatives and public domain knowledge networks. When sponsored, this concept will be tested by 12 confirmed clinical research and public health organizations in six countries. The potential impact of this platform is reduced drug discovery and public health innovation lag time and improved clinical trial interventions, due to reliable clinical intelligence and bio-surveillance across all phases of the clinical innovation process.

Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop.

PubMed

Jagodnik, Kathleen M; Koplev, Simon; Jenkins, Sherry L; Ohno-Machado, Lucila; Paten, Benedict; Schurer, Stephan C; Dumontier, Michel; Verborgh, Ruben; Bui, Alex; Ping, Peipei; McKenna, Neil J; Madduri, Ravi; Pillai, Ajay; Ma'ayan, Avi

2017-07-01

The volume and diversity of data in biomedical research have been rapidly increasing in recent years. While such data hold significant promise for accelerating discovery, their use entails many challenges including: the need for adequate computational infrastructure, secure processes for data sharing and access, tools that allow researchers to find and integrate diverse datasets, and standardized methods of analysis. These are just some elements of a complex ecosystem that needs to be built to support the rapid accumulation of these data. The NIH Big Data to Knowledge (BD2K) initiative aims to facilitate digitally enabled biomedical research. Within the BD2K framework, the Commons initiative is intended to establish a virtual environment that will facilitate the use, interoperability, and discoverability of shared digital objects used for research. The BD2K Commons Framework Pilots Working Group (CFPWG) was established to clarify goals and work on pilot projects that address existing gaps toward realizing the vision of the BD2K Commons. This report reviews highlights from a two-day meeting involving the BD2K CFPWG to provide insights on trends and considerations in advancing Big Data science for biomedical research in the United States. Copyright © 2017 Elsevier Inc. All rights reserved.

The relation between prior knowledge and students' collaborative discovery learning processes

NASA Astrophysics Data System (ADS)

Gijlers, Hannie; de Jong, Ton

2005-03-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction with the environment was logged. Based on students' individual judgments of the truth-value and testability of a series of domain-specific propositions, a detailed description of the knowledge configuration for each dyad was created before they entered the learning environment. Qualitative analyses of two dialogues illustrated that prior knowledge influences the discovery learning processes, and knowledge development in a pair of students. Assessments of student and dyad definitional (domain-specific) knowledge, generic (mathematical and graph) knowledge, and generic (discovery) skills were related to the students' dialogue in different discovery learning processes. Results show that a high level of definitional prior knowledge is positively related to the proportion of communication regarding the interpretation of results. Heterogeneity with respect to generic prior knowledge was positively related to the number of utterances made in the discovery process categories hypotheses generation and experimentation. Results of the qualitative analyses indicated that collaboration between extremely heterogeneous dyads is difficult when the high achiever is not willing to scaffold information and work in the low achiever's zone of proximal development.

PKDE4J: Entity and relation extraction for public knowledge discovery.

PubMed

Song, Min; Kim, Won Chul; Lee, Dahee; Heo, Go Eun; Kang, Keun Young

2015-10-01

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction. Copyright © 2015 Elsevier Inc. All rights reserved.

Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience.

PubMed

Matiasz, Nicholas J; Wood, Justin; Wang, Wei; Silva, Alcino J; Hsu, William

2017-01-01

Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained. Here, we outline how epistemological principles and graphical representations of causality can be used to formalize experiment planning toward causal discovery. We outline two complementary approaches to experiment planning: one that quantifies evidence per the principles of convergence and consistency, and another that quantifies uncertainty using logical representations of constraints on causal structure. These approaches operationalize experiment planning as the search for an experiment that either maximizes evidence or minimizes uncertainty. Despite work in laboratory automation, humans must still plan experiments and will likely continue to do so for some time. There is thus a great need for experiment-planning frameworks that are not only amenable to machine computation but also useful as aids in human reasoning.

Modeling technology innovation: how science, engineering, and industry methods can combine to generate beneficial socioeconomic impacts.

PubMed

Stone, Vathsala I; Lane, Joseph P

2012-05-16

Government-sponsored science, technology, and innovation (STI) programs support the socioeconomic aspects of public policies, in addition to expanding the knowledge base. For example, beneficial healthcare services and devices are expected to result from investments in research and development (R&D) programs, which assume a causal link to commercial innovation. Such programs are increasingly held accountable for evidence of impact-that is, innovative goods and services resulting from R&D activity. However, the absence of comprehensive models and metrics skews evidence gathering toward bibliometrics about research outputs (published discoveries), with less focus on transfer metrics about development outputs (patented prototypes) and almost none on econometrics related to production outputs (commercial innovations). This disparity is particularly problematic for the expressed intent of such programs, as most measurable socioeconomic benefits result from the last category of outputs. This paper proposes a conceptual framework integrating all three knowledge-generating methods into a logic model, useful for planning, obtaining, and measuring the intended beneficial impacts through the implementation of knowledge in practice. Additionally, the integration of the Context-Input-Process-Product (CIPP) model of evaluation proactively builds relevance into STI policies and programs while sustaining rigor. The resulting logic model framework explicitly traces the progress of knowledge from inputs, following it through the three knowledge-generating processes and their respective knowledge outputs (discovery, invention, innovation), as it generates the intended socio-beneficial impacts. It is a hybrid model for generating technology-based innovations, where best practices in new product development merge with a widely accepted knowledge-translation approach. Given the emphasis on evidence-based practice in the medical and health fields and "bench to bedside" expectations for knowledge transfer, sponsors and grantees alike should find the model useful for planning, implementing, and evaluating innovation processes. High-cost/high-risk industries like healthcare require the market deployment of technology-based innovations to improve domestic society in a global economy. An appropriate balance of relevance and rigor in research, development, and production is crucial to optimize the return on public investment in such programs. The technology-innovation process needs a comprehensive operational model to effectively allocate public funds and thereby deliberately and systematically accomplish socioeconomic benefits.

Modeling technology innovation: How science, engineering, and industry methods can combine to generate beneficial socioeconomic impacts

PubMed Central

2012-01-01

Background Government-sponsored science, technology, and innovation (STI) programs support the socioeconomic aspects of public policies, in addition to expanding the knowledge base. For example, beneficial healthcare services and devices are expected to result from investments in research and development (R&D) programs, which assume a causal link to commercial innovation. Such programs are increasingly held accountable for evidence of impact—that is, innovative goods and services resulting from R&D activity. However, the absence of comprehensive models and metrics skews evidence gathering toward bibliometrics about research outputs (published discoveries), with less focus on transfer metrics about development outputs (patented prototypes) and almost none on econometrics related to production outputs (commercial innovations). This disparity is particularly problematic for the expressed intent of such programs, as most measurable socioeconomic benefits result from the last category of outputs. Methods This paper proposes a conceptual framework integrating all three knowledge-generating methods into a logic model, useful for planning, obtaining, and measuring the intended beneficial impacts through the implementation of knowledge in practice. Additionally, the integration of the Context-Input-Process-Product (CIPP) model of evaluation proactively builds relevance into STI policies and programs while sustaining rigor. Results The resulting logic model framework explicitly traces the progress of knowledge from inputs, following it through the three knowledge-generating processes and their respective knowledge outputs (discovery, invention, innovation), as it generates the intended socio-beneficial impacts. It is a hybrid model for generating technology-based innovations, where best practices in new product development merge with a widely accepted knowledge-translation approach. Given the emphasis on evidence-based practice in the medical and health fields and “bench to bedside” expectations for knowledge transfer, sponsors and grantees alike should find the model useful for planning, implementing, and evaluating innovation processes. Conclusions High-cost/high-risk industries like healthcare require the market deployment of technology-based innovations to improve domestic society in a global economy. An appropriate balance of relevance and rigor in research, development, and production is crucial to optimize the return on public investment in such programs. The technology-innovation process needs a comprehensive operational model to effectively allocate public funds and thereby deliberately and systematically accomplish socioeconomic benefits. PMID:22591638

Information Fusion for Natural and Man-Made Disasters

DTIC Science & Technology

2007-01-31

comprehensively large, and metaphysically accurate model of situations, through which specific tasks such as situation assessment, knowledge discovery , or the...significance” is always context specific. Event discovery is a very important element of the HLF process, which can lead to knowledge discovery about...expected, given the current state of knowledge . Examples of such behavior may include discovery of a new aggregate or situation, a specific pattern of

Knowledge Discovery for Smart Grid Operation, Control, and Situation Awareness -- A Big Data Visualization Platform

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

Gu, Yi; Jiang, Huaiguang; Zhang, Yingchen

In this paper, a big data visualization platform is designed to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. The spawn of smart sensors at both grid side and customer side can provide large volume of heterogeneous data that collect information in all time spectrums. Extracting useful knowledge from this big-data poll is still challenging. In this paper, the Apache Spark, an open source cluster computing framework, is used to process the big-data to effectively discover the hidden knowledge. A high-speed communication architecture utilizing the Open System Interconnection (OSI) model is designed to transmitmore » the data to a visualization platform. This visualization platform uses Google Earth, a global geographic information system (GIS) to link the geological information with the SG knowledge and visualize the information in user defined fashion. The University of Denver's campus grid is used as a SG test bench and several demonstrations are presented for the proposed platform.« less

A New System To Support Knowledge Discovery: Telemakus.

ERIC Educational Resources Information Center

Revere, Debra; Fuller, Sherrilynne S.; Bugni, Paul F.; Martin, George M.

2003-01-01

The Telemakus System builds on the areas of concept representation, schema theory, and information visualization to enhance knowledge discovery from scientific literature. This article describes the underlying theories and an overview of a working implementation designed to enhance the knowledge discovery process through retrieval, visual and…

Knowledge Discovery as an Aid to Organizational Creativity.

ERIC Educational Resources Information Center

Siau, Keng

2000-01-01

This article presents the concept of knowledge discovery, a process of searching for associations in large volumes of computer data, as an aid to creativity. It then discusses the various techniques in knowledge discovery. Mednick's associative theory of creative thought serves as the theoretical foundation for this research. (Contains…

Advances in Knowledge Discovery and Data Mining 21st Pacific Asia Conference, PAKDD 2017 Held in Jeju, South Korea, May 23 26, 2017. Proceedings Part I, Part II.

DTIC Science & Technology

2017-06-27

From - To) 05-27-2017 Final 17-03-2017 - 15-03-2018 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER FA2386-17-1-0102 Advances in Knowledge Discovery and...Springer; Switzerland. 14. ABSTRACT The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference...in the areas of knowledge discovery and data mining (KDD). We had three keynote speeches, delivered by Sang Cha from Seoul National University

Early repositioning through compound set enrichment analysis: a knowledge-recycling strategy.

PubMed

Temesi, Gergely; Bolgár, Bence; Arany, Adám; Szalai, Csaba; Antal, Péter; Mátyus, Péter

2014-04-01

Despite famous serendipitous drug repositioning success stories, systematic projects have not yet delivered the expected results. However, repositioning technologies are gaining ground in different phases of routine drug development, together with new adaptive strategies. We demonstrate the power of the compound information pool, the ever-growing heterogeneous information repertoire of approved drugs and candidates as an invaluable catalyzer in this transition. Systematic, computational utilization of this information pool for candidates in early phases is an open research problem; we propose a novel application of the enrichment analysis statistical framework for fusion of this information pool, specifically for the prediction of indications. Pharmaceutical consequences are formulated for a systematic and continuous knowledge recycling strategy, utilizing this information pool throughout the drug-discovery pipeline.

Sculpting the brain

PubMed Central

Garcia-Lopez, Pablo

2012-01-01

Neuroculture, conceived as the reciprocal interaction between neuroscience and different areas of human knowledge is influencing our lives under the prism of the latest neuroscientific discoveries. Simultaneously, neuroculture can create new models of thinking that can significantly impact neuroscientists' daily practice. Especially interesting is the interaction that takes place between neuroscience and the arts. This interaction takes place at different, infinite levels and contexts. I contextualize my work inside this neurocultural framework. Through my artwork, I try to give a more natural vision of the human brain, which could help to develop a more humanistic culture. PMID:22363275

The Relation between Prior Knowledge and Students' Collaborative Discovery Learning Processes

ERIC Educational Resources Information Center

Gijlers, Hannie; de Jong, Ton

2005-01-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction…

Framework for development of physician competencies in genomic medicine: report of the Competencies Working Group of the Inter-Society Coordinating Committee for Physician Education in Genomics.

PubMed

Korf, Bruce R; Berry, Anna B; Limson, Melvin; Marian, Ali J; Murray, Michael F; O'Rourke, P Pearl; Passamani, Eugene R; Relling, Mary V; Tooker, John; Tsongalis, Gregory J; Rodriguez, Laura L

2014-11-01

Completion of the Human Genome Project, in conjunction with dramatic reductions in the cost of DNA sequencing and advances in translational research, is gradually ushering genomic discoveries and technologies into the practice of medicine. The rapid pace of these advances is opening up a gap between the knowledge available about the clinical relevance of genomic information and the ability of clinicians to include such information in their medical practices. This educational gap threatens to be rate limiting to the clinical adoption of genomics in medicine. Solutions will require not only a better understanding of the clinical implications of genetic discoveries but also training in genomics at all levels of professional development, including for individuals in formal training and others who long ago completed such training. The National Human Genome Research Institute has convened the Inter-Society Coordinating Committee for Physician Education in Genomics (ISCC) to develop and share best practices in the use of genomics in medicine. The ISCC has developed a framework for development of genomics practice competencies that may serve as a starting point for formulation of competencies for physicians in various medical disciplines.

An Ecological Framework of the Human Virome Provides Classification of Current Knowledge and Identifies Areas of Forthcoming Discovery

PubMed Central

Parker, Michael T.

2016-01-01

Recent advances in sequencing technologies have opened the door for the classification of the human virome. While taxonomic classification can be applied to the viruses identified in such studies, this gives no information as to the type of interaction the virus has with the host. As follow-up studies are performed to address these questions, the description of these virus-host interactions would be greatly enriched by applying a standard set of definitions that typify them. This paper describes a framework with which all members of the human virome can be classified based on principles of ecology. The scaffold not only enables categorization of the human virome, but can also inform research aimed at identifying novel virus-host interactions. PMID:27698618

SemaTyP: a knowledge graph based literature mining method for drug discovery.

PubMed

Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

Knowledge discovery from data as a framework to decision support in medical domains

PubMed Central

Gibert, Karina

2009-01-01

Introduction Knowledge discovery from data (KDD) is a multidisciplinary discipline which appeared in 1996 for “non trivial identifying of valid, novel, potentially useful, ultimately understandable patterns in data”. Pre-treatment of data and post-processing is as important as the data exploitation (Data Mining) itself. Different analysis techniques can be properly combined to produce explicit knowledge from data. Methods Hybrid KDD methodologies combining Artificial Intelligence with Statistics and visualization have been used to identify patterns in complex medical phenomena: experts provide prior knowledge (pK); it biases the search of distinguishable groups of homogeneous objects; support-interpretation tools (CPG) assisted experts in conceptualization and labelling of discovered patterns, consistently with pK. Results Patterns of dependency in mental disabilities supported decision-making on legislation of the Spanish Dependency Law in Catalonia. Relationships between type of neurorehabilitation treatment and patterns of response for brain damage are assessed. Patterns of the perceived QOL along time are used in spinal cord lesion to improve social inclusion. Conclusion Reality is more and more complex and classical data analyses are not powerful enough to model it. New methodologies are required including multidisciplinarity and stressing on production of understandable models. Interaction with the experts is critical to generate meaningful results which can really support decision-making, particularly convenient transferring the pK to the system, as well as interpreting results in close interaction with experts. KDD is a valuable paradigm, particularly when facing very complex domains, not well understood yet, like many medical phenomena.

Big data analytics in immunology: a knowledge-based approach.

PubMed

Zhang, Guang Lan; Sun, Jing; Chitkushev, Lou; Brusic, Vladimir

2014-01-01

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

A New Student Performance Analysing System Using Knowledge Discovery in Higher Educational Databases

ERIC Educational Resources Information Center

Guruler, Huseyin; Istanbullu, Ayhan; Karahasan, Mehmet

2010-01-01

Knowledge discovery is a wide ranged process including data mining, which is used to find out meaningful and useful patterns in large amounts of data. In order to explore the factors having impact on the success of university students, knowledge discovery software, called MUSKUP, has been developed and tested on student data. In this system a…

A framework to analyse gender bias in epidemiological research

PubMed Central

Ruiz‐Cantero, María Teresa; Vives‐Cases, Carmen; Artazcoz, Lucía; Delgado, Ana; del Mar García Calvente, Maria; Miqueo, Consuelo; Montero, Isabel; Ortiz, Rocío; Ronda, Elena; Ruiz, Isabel; Valls, Carme

2007-01-01

The design and analysis of research may cause systematic gender dependent errors to be produced in results because of gender insensitivity or androcentrism. Gender bias in research could be defined as a systematically erroneous gender dependent approach related to social construct, which incorrectly regards women and men as similar/different. Most gender bias can be found in the context of discovery (development of hypotheses), but it has also been found in the context of justification (methodological process), which must be improved. In fact, one of the main effects of gender bias in research is partial or incorrect knowledge in the results, which are systematically different from the real values. This paper discusses some forms of conceptual and methodological bias that may affect women's health. It proposes a framework to analyse gender bias in the design and analysis of research carried out on women's and men's health problems, and on specific women's health issues. Using examples, the framework aims to show the different theoretical perspectives in a social or clinical research context where forms of selection, measurement and confounding bias are produced as a result of gender insensitivity. Finally, this paper underlines the importance of re‐examining results so that they may be reinterpreted to produce new gender based knowledge. PMID:18000118

A framework to analyse gender bias in epidemiological research.

PubMed

Ruiz-Cantero, María Teresa; Vives-Cases, Carmen; Artazcoz, Lucía; Delgado, Ana; García Calvente, Maria Mar; Miqueo, Consuelo; Montero, Isabel; Ortiz, Rocío; Ronda, Elena; Ruiz, Isabel; Valls, Carme

2007-12-01

The design and analysis of research may cause systematic gender dependent errors to be produced in results because of gender insensitivity or androcentrism. Gender bias in research could be defined as a systematically erroneous gender dependent approach related to social construct, which incorrectly regards women and men as similar/different. Most gender bias can be found in the context of discovery (development of hypotheses), but it has also been found in the context of justification (methodological process), which must be improved. In fact, one of the main effects of gender bias in research is partial or incorrect knowledge in the results, which are systematically different from the real values. This paper discusses some forms of conceptual and methodological bias that may affect women's health. It proposes a framework to analyse gender bias in the design and analysis of research carried out on women's and men's health problems, and on specific women's health issues. Using examples, the framework aims to show the different theoretical perspectives in a social or clinical research context where forms of selection, measurement and confounding bias are produced as a result of gender insensitivity. Finally, this paper underlines the importance of re-examining results so that they may be reinterpreted to produce new gender based knowledge.

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.

Knowledge Discovery in Databases.

ERIC Educational Resources Information Center

Norton, M. Jay

1999-01-01

Knowledge discovery in databases (KDD) revolves around the investigation and creation of knowledge, processes, algorithms, and mechanisms for retrieving knowledge from data collections. The article is an introductory overview of KDD. The rationale and environment of its development and applications are discussed. Issues related to database design…

A Research Framework for Reducing Preventable Patient Harm

PubMed Central

Weinstein, Robert; Cardo, Denise M.; Goeschel, Christine A.; Berenholtz, Sean M.; Saint, Sanjay; Jernigan, John A.

2011-01-01

Programs to reduce central line–associated bloodstream infections (CLABSIs) have improved the safety of hospitalized patients. Efforts are underway to disseminate these successes broadly to reduce other types of hospital-acquired infectious and noninfectious preventable harms. Unfortunately, the ability to broadly measure and prevent other types of preventable harms, especially infectious harms, needs enhancement. Moreover, an overarching research framework for creating and integrating evidence will help expedite the development of national prevention programs. This article outlines a 5-phase translational (T) framework to develop robust research programs that reduce preventable harm, as follows: phase T0, discover opportunities and approaches to prevent adverse health care events; phase T1, use T0 discoveries to develop and test interventions on a small scale; phase T2, broaden and strengthen the evidence base for promising interventions to develop evidence-based guidelines; phase T3, translate guidelines into clinical practice; and phase T4, implement and evaluate T3 work on a national and international scale. Policy makers should use this framework to fill in the knowledge gaps, coordinate efforts among federal agencies, and prioritize research funding. PMID:21258104

Towards a semantics-based approach in the development of geographic portals

NASA Astrophysics Data System (ADS)

Athanasis, Nikolaos; Kalabokidis, Kostas; Vaitis, Michail; Soulakellis, Nikolaos

2009-02-01

As the demand for geospatial data increases, the lack of efficient ways to find suitable information becomes critical. In this paper, a new methodology for knowledge discovery in geographic portals is presented. Based on the Semantic Web, our approach exploits the Resource Description Framework (RDF) in order to describe the geoportal's information with ontology-based metadata. When users traverse from page to page in the portal, they take advantage of the metadata infrastructure to navigate easily through data of interest. New metadata descriptions are published in the geoportal according to the RDF schemas.

Workflow based framework for life science informatics.

PubMed

Tiwari, Abhishek; Sekhar, Arvind K T

2007-10-01

Workflow technology is a generic mechanism to integrate diverse types of available resources (databases, servers, software applications and different services) which facilitate knowledge exchange within traditionally divergent fields such as molecular biology, clinical research, computational science, physics, chemistry and statistics. Researchers can easily incorporate and access diverse, distributed tools and data to develop their own research protocols for scientific analysis. Application of workflow technology has been reported in areas like drug discovery, genomics, large-scale gene expression analysis, proteomics, and system biology. In this article, we have discussed the existing workflow systems and the trends in applications of workflow based systems.

Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping.

PubMed

Soh, Jung; Turinsky, Andrei L; Trinh, Quang M; Chang, Jasmine; Sabhaney, Ajay; Dong, Xiaoli; Gordon, Paul Mk; Janzen, Ryan Pw; Hau, David; Xia, Jianguo; Wishart, David S; Sensen, Christoph W

2009-01-01

We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.

Knowledge discovery with classification rules in a cardiovascular dataset.

PubMed

Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

2005-12-01

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

Designing for Discovery Learning of Complexity Principles of Congestion by Driving Together in the TrafficJams Simulation

ERIC Educational Resources Information Center

Levy, Sharona T.; Peleg, Ran; Ofeck, Eyal; Tabor, Naamit; Dubovi, Ilana; Bluestein, Shiri; Ben-Zur, Hadar

2018-01-01

We propose and evaluate a framework supporting collaborative discovery learning of complex systems. The framework blends five design principles: (1) individual action: amidst (2) social interactions; challenged with (3) multiple tasks; set in (4) a constrained interactive learning environment that draws attention to (5) highlighted target…

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

PubMed Central

Sacchi, L.

2016-01-01

Summary Objectives We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data. PMID:27488403

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.

PubMed

Sacchi, L; Holmes, J H

2016-08-02

We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992- 2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

Communication in Collaborative Discovery Learning

ERIC Educational Resources Information Center

Saab, Nadira; van Joolingen, Wouter R.; van Hout-Wolters, Bernadette H. A. M.

2005-01-01

Background: Constructivist approaches to learning focus on learning environments in which students have the opportunity to construct knowledge themselves, and negotiate this knowledge with others. "Discovery learning" and "collaborative learning" are examples of learning contexts that cater for knowledge construction processes. We introduce a…

Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management

PubMed Central

Li, Yan; Thomas, Manoj; Osei-Bryson, Kweku-Muata; Levy, Jason

2016-01-01

With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environmental risk management problem is essential. In this emerging field, there is limited research dealing with the use of decision support to elicit environmental risk management (ERM) objectives and identify analytical goals from ERM decision makers. In this paper, we address problem formulation in the ERM understanding phase of the KDDA process. We build a DM3 ontology to capture ERM objectives and to inference analytical goals and associated analytical techniques. A framework to assist decision making in the problem formulation process is developed. It is shown how the ontology-based knowledge system can provide structured guidance to retrieve relevant knowledge during problem formulation. The importance of not only operationalizing the KDDA approach in a real-world environment but also evaluating the effectiveness of the proposed procedure is emphasized. We demonstrate how ontology inferencing may be used to discover analytical goals and techniques by conceptualizing Hazardous Air Pollutants (HAPs) exposure shifts based on a multilevel analysis of the level of urbanization (and related economic activity) and the degree of Socio-Economic Deprivation (SED) at the local neighborhood level. The HAPs case highlights not only the role of complexity in problem formulation but also the need for integrating data from multiple sources and the importance of employing appropriate KDDA modeling techniques. Challenges and opportunities for KDDA are summarized with an emphasis on environmental risk management and HAPs. PMID:27983713

Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management.

PubMed

Li, Yan; Thomas, Manoj; Osei-Bryson, Kweku-Muata; Levy, Jason

2016-12-15

With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environmental risk management problem is essential. In this emerging field, there is limited research dealing with the use of decision support to elicit environmental risk management (ERM) objectives and identify analytical goals from ERM decision makers. In this paper, we address problem formulation in the ERM understanding phase of the KDDA process. We build a DM³ ontology to capture ERM objectives and to inference analytical goals and associated analytical techniques. A framework to assist decision making in the problem formulation process is developed. It is shown how the ontology-based knowledge system can provide structured guidance to retrieve relevant knowledge during problem formulation. The importance of not only operationalizing the KDDA approach in a real-world environment but also evaluating the effectiveness of the proposed procedure is emphasized. We demonstrate how ontology inferencing may be used to discover analytical goals and techniques by conceptualizing Hazardous Air Pollutants (HAPs) exposure shifts based on a multilevel analysis of the level of urbanization (and related economic activity) and the degree of Socio-Economic Deprivation (SED) at the local neighborhood level. The HAPs case highlights not only the role of complexity in problem formulation but also the need for integrating data from multiple sources and the importance of employing appropriate KDDA modeling techniques. Challenges and opportunities for KDDA are summarized with an emphasis on environmental risk management and HAPs.

KnowEnG: a knowledge engine for genomics.

PubMed

Sinha, Saurabh; Song, Jun; Weinshilboum, Richard; Jongeneel, Victor; Han, Jiawei

2015-11-01

We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of "Knowledge Engine for Genomics" (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the "Knowledge Network" that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

e-IQ and IQ knowledge mining for generalized LDA

NASA Astrophysics Data System (ADS)

Jenkins, Jeffrey; van Bergem, Rutger; Sweet, Charles; Vietsch, Eveline; Szu, Harold

2015-05-01

How can the human brain uncover patterns, associations and features in real-time, real-world data? There must be a general strategy used to transform raw signals into useful features, but representing this generalization in the context of our information extraction tool set is lacking. In contrast to Big Data (BD), Large Data Analysis (LDA) has become a reachable multi-disciplinary goal in recent years due in part to high performance computers and algorithm development, as well as the availability of large data sets. However, the experience of Machine Learning (ML) and information communities has not been generalized into an intuitive framework that is useful to researchers across disciplines. The data exploration phase of data mining is a prime example of this unspoken, ad-hoc nature of ML - the Computer Scientist works with a Subject Matter Expert (SME) to understand the data, and then build tools (i.e. classifiers, etc.) which can benefit the SME and the rest of the researchers in that field. We ask, why is there not a tool to represent information in a meaningful way to the researcher asking the question? Meaning is subjective and contextual across disciplines, so to ensure robustness, we draw examples from several disciplines and propose a generalized LDA framework for independent data understanding of heterogeneous sources which contribute to Knowledge Discovery in Databases (KDD). Then, we explore the concept of adaptive Information resolution through a 6W unsupervised learning methodology feedback system. In this paper, we will describe the general process of man-machine interaction in terms of an asymmetric directed graph theory (digging for embedded knowledge), and model the inverse machine-man feedback (digging for tacit knowledge) as an ANN unsupervised learning methodology. Finally, we propose a collective learning framework which utilizes a 6W semantic topology to organize heterogeneous knowledge and diffuse information to entities within a society in a personalized way.

Mississippi State University Center for Air Sea Technology. FY93 and FY 94 Research Program in Navy Ocean Modeling and Prediction

DTIC Science & Technology

1994-09-30

relational versus object oriented DBMS, knowledge discovery, data models, rnetadata, data filtering, clustering techniques, and synthetic data. A secondary...The first was the investigation of Al/ES Lapplications (knowledge discovery, data mining, and clustering ). Here CAST collabo.rated with Dr. Fred Petry...knowledge discovery system based on clustering techniques; implemented an on-line data browser to the DBMS; completed preliminary efforts to apply object

Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach

PubMed Central

Song, Min

2016-01-01

In biomedicine, scientific literature is a valuable source for knowledge discovery. Mining knowledge from textual data has become an ever important task as the volume of scientific literature is growing unprecedentedly. In this paper, we propose a framework for examining a certain disease based on existing information provided by scientific literature. Disease-related entities that include diseases, drugs, and genes are systematically extracted and analyzed using a three-level network-based approach. A paper-entity network and an entity co-occurrence network (macro-level) are explored and used to construct six entity specific networks (meso-level). Important diseases, drugs, and genes as well as salient entity relations (micro-level) are identified from these networks. Results obtained from the literature-based literature mining can serve to assist clinical applications. PMID:27195695

Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.

PubMed

Girardi, Dominic; Küng, Josef; Kleiser, Raimund; Sonnberger, Michael; Csillag, Doris; Trenkler, Johannes; Holzinger, Andreas

2016-09-01

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.

History and utility of zeolite framework-type discovery from a data-science perspective

DOE PAGES

Zimmermann, Nils E. R.; Haranczyk, Maciej

2016-05-02

Mature applications such as fluid catalytic cracking and hydrocracking rely critically on early zeolite structures. With a data-driven approach, we find that the discovery of exceptional zeolite framework types around the new millennium was spurred by exciting new utilization routes. The promising processes have yet not been successfully implemented (“valley of death” effect), mainly because of the lack of thermal stability of the crystals. As a result, this foreshadows limited deployability of recent zeolite discoveries that were achieved by novel crystal synthesis routes.

Plants in silico: why, why now and what?--an integrative platform for plant systems biology research.

PubMed

Zhu, Xin-Guang; Lynch, Jonathan P; LeBauer, David S; Millar, Andrew J; Stitt, Mark; Long, Stephen P

2016-05-01

A paradigm shift is needed and timely in moving plant modelling from largely isolated efforts to a connected community endeavour that can take full advantage of advances in computer science and in mechanistic understanding of plant processes. Plants in silico (Psi) envisions a digital representation of layered dynamic modules, linking from gene networks and metabolic pathways through to cellular organization, tissue, organ and whole plant development, together with resource capture and use efficiency in dynamic competitive environments, ultimately allowing a mechanistically rich simulation of the plant or of a community of plants in silico. The concept is to integrate models or modules from different layers of organization spanning from genome to phenome to ecosystem in a modular framework allowing the use of modules of varying mechanistic detail representing the same biological process. Developments in high-performance computing, functional knowledge of plants, the internet and open-source version controlled software make achieving the concept realistic. Open source will enhance collaboration and move towards testing and consensus on quantitative theoretical frameworks. Importantly, Psi provides a quantitative knowledge framework where the implications of a discovery at one level, for example, single gene function or developmental response, can be examined at the whole plant or even crop and natural ecosystem levels. © 2015 The Authors Plant, Cell & Environment Published by John Wiley & Sons Ltd.

Nursing Routine Data as a Basis for Association Analysis in the Domain of Nursing Knowledge

PubMed Central

Sellemann, Björn; Stausberg, Jürgen; Hübner, Ursula

2012-01-01

This paper describes the data mining method of association analysis within the framework of Knowledge Discovery in Databases (KDD) with the aim to identify standard patterns of nursing care. The approach is application-oriented and used on nursing routine data of the method LEP nursing 2. The increasing use of information technology in hospitals, especially of nursing information systems, requires the storage of large data sets, which hitherto have not always been analyzed adequately. Three association analyses for the days of admission, surgery and discharge, have been performed. The results of almost 1.5 million generated association rules indicate that it is valid to apply association analysis to nursing routine data. All rules are semantically trivial, since they reflect existing knowledge from the domain of nursing. This may be due either to the method LEP Nursing 2, or to the nursing activities themselves. Nonetheless, association analysis may in future become a useful analytical tool on the basis of structured nursing routine data. PMID:24199122

Clinical Adoption of Prognostic Biomarkers The Case for Heart Failure

PubMed Central

Kalogeropoulos, Andreas P.; Georgiopoulou, Vasiliki V.; Butler, Javed

2013-01-01

The recent explosion of scientific knowledge and technological progress has led to the discovery of a large array of circulating molecules commonly referred to as biomarkers. Biomarkers in heart failure research have been used to provide pathophysiological insights, aid in establishing the diagnosis, refine prognosis, guide management, and target treatment. However, beyond diagnostic applications of natriuretic peptides, there are currently few widely recognized applications for biomarkers in heart failure. This represents a remarkable discordance considering the number of molecules that have been shown to correlate with outcomes, refine risk prediction, or track disease severity in heart failure in the past decade. In this article, we use a broad framework proposed for cardiovascular risk markers to summarize the current state of biomarker development for heart failure patients. We utilize this framework to identify the challenges of biomarker adoption for risk prediction, disease management, and treatment selection for heart failure and suggest considerations for future research. PMID:22824105

Knowledge Discovery in Textual Documentation: Qualitative and Quantitative Analyses.

ERIC Educational Resources Information Center

Loh, Stanley; De Oliveira, Jose Palazzo M.; Gastal, Fabio Leite

2001-01-01

Presents an application of knowledge discovery in texts (KDT) concerning medical records of a psychiatric hospital. The approach helps physicians to extract knowledge about patients and diseases that may be used for epidemiological studies, for training professionals, and to support physicians to diagnose and evaluate diseases. (Author/AEF)

The Genetic Basis of Mendelian Phenotypes: Discoveries, Challenges, and Opportunities

PubMed Central

Chong, Jessica X.; Buckingham, Kati J.; Jhangiani, Shalini N.; Boehm, Corinne; Sobreira, Nara; Smith, Joshua D.; Harrell, Tanya M.; McMillin, Margaret J.; Wiszniewski, Wojciech; Gambin, Tomasz; Coban Akdemir, Zeynep H.; Doheny, Kimberly; Scott, Alan F.; Avramopoulos, Dimitri; Chakravarti, Aravinda; Hoover-Fong, Julie; Mathews, Debra; Witmer, P. Dane; Ling, Hua; Hetrick, Kurt; Watkins, Lee; Patterson, Karynne E.; Reinier, Frederic; Blue, Elizabeth; Muzny, Donna; Kircher, Martin; Bilguvar, Kaya; López-Giráldez, Francesc; Sutton, V. Reid; Tabor, Holly K.; Leal, Suzanne M.; Gunel, Murat; Mane, Shrikant; Gibbs, Richard A.; Boerwinkle, Eric; Hamosh, Ada; Shendure, Jay; Lupski, James R.; Lifton, Richard P.; Valle, David; Nickerson, Deborah A.; Bamshad, Michael J.

2015-01-01

Discovering the genetic basis of a Mendelian phenotype establishes a causal link between genotype and phenotype, making possible carrier and population screening and direct diagnosis. Such discoveries also contribute to our knowledge of gene function, gene regulation, development, and biological mechanisms that can be used for developing new therapeutics. As of February 2015, 2,937 genes underlying 4,163 Mendelian phenotypes have been discovered, but the genes underlying ∼50% (i.e., 3,152) of all known Mendelian phenotypes are still unknown, and many more Mendelian conditions have yet to be recognized. This is a formidable gap in biomedical knowledge. Accordingly, in December 2011, the NIH established the Centers for Mendelian Genomics (CMGs) to provide the collaborative framework and infrastructure necessary for undertaking large-scale whole-exome sequencing and discovery of the genetic variants responsible for Mendelian phenotypes. In partnership with 529 investigators from 261 institutions in 36 countries, the CMGs assessed 18,863 samples from 8,838 families representing 579 known and 470 novel Mendelian phenotypes as of January 2015. This collaborative effort has identified 956 genes, including 375 not previously associated with human health, that underlie a Mendelian phenotype. These results provide insight into study design and analytical strategies, identify novel mechanisms of disease, and reveal the extensive clinical variability of Mendelian phenotypes. Discovering the gene underlying every Mendelian phenotype will require tackling challenges such as worldwide ascertainment and phenotypic characterization of families affected by Mendelian conditions, improvement in sequencing and analytical techniques, and pervasive sharing of phenotypic and genomic data among researchers, clinicians, and families. PMID:26166479

Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research.

PubMed

An, Gary C

2010-01-01

The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate "systems" processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing "thought experiments" to fill those gaps. Agent-based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent-based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community-wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of "knowledge ecologies," where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.

A collaborative filtering-based approach to biomedical knowledge discovery.

PubMed

Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R; Krzywinski, Martin; Jones, Steven J M; Wren, Jonathan

2018-02-15

The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Analysis Commons, A Team Approach to Discovery in a Big-Data Environment for Genetic Epidemiology

PubMed Central

Brody, Jennifer A.; Morrison, Alanna C.; Bis, Joshua C.; O'Connell, Jeffrey R.; Brown, Michael R.; Huffman, Jennifer E.; Ames, Darren C.; Carroll, Andrew; Conomos, Matthew P.; Gabriel, Stacey; Gibbs, Richard A.; Gogarten, Stephanie M.; Gupta, Namrata; Jaquish, Cashell E.; Johnson, Andrew D.; Lewis, Joshua P.; Liu, Xiaoming; Manning, Alisa K.; Papanicolaou, George J.; Pitsillides, Achilleas N.; Rice, Kenneth M.; Salerno, William; Sitlani, Colleen M.; Smith, Nicholas L.; Heckbert, Susan R.; Laurie, Cathy C.; Mitchell, Braxton D.; Vasan, Ramachandran S.; Rich, Stephen S.; Rotter, Jerome I.; Wilson, James G.; Boerwinkle, Eric; Psaty, Bruce M.; Cupples, L. Adrienne

2017-01-01

Summary paragraph The exploding volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created a cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multi-center WGS analyses, including data sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation, and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for transforming WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations. PMID:29074945

A platform for discovery: The University of Pennsylvania Integrated Neurodegenerative Disease Biobank

PubMed Central

Toledo, Jon B.; Van Deerlin, Vivianna M.; Lee, Edward B.; Suh, EunRan; Baek, Young; Robinson, John L.; Xie, Sharon X.; McBride, Jennifer; Wood, Elisabeth M.; Schuck, Theresa; Irwin, David J.; Gross, Rachel G.; Hurtig, Howard; McCluskey, Leo; Elman, Lauren; Karlawish, Jason; Schellenberg, Gerard; Chen-Plotkin, Alice; Wolk, David; Grossman, Murray; Arnold, Steven E.; Shaw, Leslie M.; Lee, Virginia M.-Y.; Trojanowski, John Q.

2014-01-01

Neurodegenerative diseases (NDs) are defined by the accumulation of abnormal protein deposits in the central nervous system (CNS), and only neuropathological examination enables a definitive diagnosis. Brain banks and their associated scientific programs have shaped the actual knowledge of NDs, identifying and characterizing the CNS deposits that define new diseases, formulating staging schemes, and establishing correlations between neuropathological changes and clinical features. However, brain banks have evolved to accommodate the banking of biofluids as well as DNA and RNA samples. Moreover, the value of biobanks is greatly enhanced if they link all the multidimensional clinical and laboratory information of each case, which is accomplished, optimally, using systematic and standardized operating procedures, and in the framework of multidisciplinary teams with the support of a flexible and user-friendly database system that facilitates the sharing of information of all the teams in the network. We describe a biobanking system that is a platform for discovery research at the Center for Neurodegenerative Disease Research at the University of Pennsylvania. PMID:23978324

Building Faculty Capacity through the Learning Sciences

ERIC Educational Resources Information Center

Moy, Elizabeth; O'Sullivan, Gerard; Terlecki, Melissa; Jernstedt, Christian

2014-01-01

Discoveries in the learning sciences (especially in neuroscience) have yielded a rich and growing body of knowledge about how students learn, yet this knowledge is only half of the story. The other half is "know how," i.e. the application of this knowledge. For faculty members, that means applying the discoveries of the learning sciences…

An epidemiological modeling and data integration framework.

PubMed

Pfeifer, B; Wurz, M; Hanser, F; Seger, M; Netzer, M; Osl, M; Modre-Osprian, R; Schreier, G; Baumgartner, C

2010-01-01

In this work, a cellular automaton software package for simulating different infectious diseases, storing the simulation results in a data warehouse system and analyzing the obtained results to generate prediction models as well as contingency plans, is proposed. The Brisbane H3N2 flu virus, which has been spreading during the winter season 2009, was used for simulation in the federal state of Tyrol, Austria. The simulation-modeling framework consists of an underlying cellular automaton. The cellular automaton model is parameterized by known disease parameters and geographical as well as demographical conditions are included for simulating the spreading. The data generated by simulation are stored in the back room of the data warehouse using the Talend Open Studio software package, and subsequent statistical and data mining tasks are performed using the tool, termed Knowledge Discovery in Database Designer (KD3). The obtained simulation results were used for generating prediction models for all nine federal states of Austria. The proposed framework provides a powerful and easy to handle interface for parameterizing and simulating different infectious diseases in order to generate prediction models and improve contingency plans for future events.

Depth of Teachers' Knowledge: Frameworks for Teachers' Knowledge of Mathematics

ERIC Educational Resources Information Center

Holmes, Vicki-Lynn

2012-01-01

This article describes seven teacher knowledge frameworks and relates these frameworks to the teaching and assessment of elementary teacher's mathematics knowledge. The frameworks classify teachers' knowledge and provide a vocabulary and common language through which knowledge can be discussed and assessed. These frameworks are categorized into…

Alchemist multimodal workflows for diabetic retinopathy research, disease prevention and investigational drug discovery.

PubMed

Riposan, Adina; Taylor, Ian; Owens, David R; Rana, Omer; Conley, Edward C

2007-01-01

In this paper we present mechanisms for imaging and spectral data discovery, as applied to the early detection of pathologic mechanisms underlying diabetic retinopathy in research and clinical trial scenarios. We discuss the Alchemist framework, built using a generic peer-to-peer architecture, supporting distributed database queries and complex search algorithms based on workflow. The Alchemist is a domain-independent search mechanism that can be applied to search and data discovery scenarios in many areas. We illustrate Alchemist's ability to perform complex searches composed as a collection of peer-to-peer overlays, Grid-based services and workflows, e.g. applied to image and spectral data discovery, as applied to the early detection and prevention of retinal disease and investigational drug discovery. The Alchemist framework is built on top of decentralised technologies and uses industry standards such as Web services and SOAP for messaging.

Synthesis and Demonstration of the Biological Relevance of sp3 -rich Scaffolds Distantly Related to Natural Product Frameworks.

PubMed

Foley, Daniel J; Craven, Philip G E; Collins, Patrick M; Doveston, Richard G; Aimon, Anthony; Talon, Romain; Churcher, Ian; von Delft, Frank; Marsden, Stephen P; Nelson, Adam

2017-10-26

The productive exploration of chemical space is an enduring challenge in chemical biology and medicinal chemistry. Natural products are biologically relevant, and their frameworks have facilitated chemical tool and drug discovery. A "top-down" synthetic approach is described that enabled a range of complex bridged intermediates to be converted with high step efficiency into 26 diverse sp 3 -rich scaffolds. The scaffolds have local natural product-like features, but are only distantly related to specific natural product frameworks. To assess biological relevance, a set of 52 fragments was prepared, and screened by high-throughput crystallography against three targets from two protein families (ATAD2, BRD1 and JMJD2D). In each case, 3D fragment hits were identified that would serve as distinctive starting points for ligand discovery. This demonstrates that frameworks that are distantly related to natural products can facilitate discovery of new biologically relevant regions within chemical space. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Synthesis and Demonstration of the Biological Relevance of sp3‐rich Scaffolds Distantly Related to Natural Product Frameworks

PubMed Central

Foley, Daniel J.; Craven, Philip G. E.; Collins, Patrick M.; Doveston, Richard G.; Aimon, Anthony; Talon, Romain; Churcher, Ian; von Delft, Frank

2017-01-01

Abstract The productive exploration of chemical space is an enduring challenge in chemical biology and medicinal chemistry. Natural products are biologically relevant, and their frameworks have facilitated chemical tool and drug discovery. A “top‐down” synthetic approach is described that enabled a range of complex bridged intermediates to be converted with high step efficiency into 26 diverse sp3‐rich scaffolds. The scaffolds have local natural product‐like features, but are only distantly related to specific natural product frameworks. To assess biological relevance, a set of 52 fragments was prepared, and screened by high‐throughput crystallography against three targets from two protein families (ATAD2, BRD1 and JMJD2D). In each case, 3D fragment hits were identified that would serve as distinctive starting points for ligand discovery. This demonstrates that frameworks that are distantly related to natural products can facilitate discovery of new biologically relevant regions within chemical space. PMID:28983993

The center for causal discovery of biomedical knowledge from big data

PubMed Central

Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-01-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. PMID:26138794

Data Discovery, Exploration, Integration and Delivery - a practical experience

NASA Astrophysics Data System (ADS)

Kirsch, Peter; Barnes, Tim; Breen, Paul

2010-05-01

To fully address the questions and issues arising within Earth Systems Science; the discovery, exploration, integration, delivery and sharing of data, metadata and services across potentially many disciplines and areas of expertise is fundamental. British Antarctic Survey (BAS) collects, manages and curates data across many fields of the geophysical and biological sciences (including upper atmospheric physics, atmospheric chemistry, meteorology, glaciology, oceanography, Polar ecology and biology). BAS, through its Polar Data Centre has an interest to construct and deliver a user-friendly, informative, and administratively low overhead interface onto these data holdings. Designing effective interfaces and frameworks onto the heterogeneous datasets described above is non-trivial. We will discuss some of our approaches and implementations; particularly those addressing the following issues: How to aid and guide the user to accurate discovery of data? Many portals do not inform users clearly enough about the datasets they actually hold. As a result the search interface by which a user is meant to discover information is often inadequate and assumes prior knowledge (for example, that the dataset you are looking for actually exists; that a particular event, campaign, research cruise took place; and that you have a specialist knowledge of the terminology in a particular field), assumptions that cannot be made in multi-disciplinary topic areas. How easily is provenance, quality, and metadata information displayed and accessed? Once informed through the portal that data is available it is often extremely difficult to assess its provenance and quality information and broader documentation (including field reports, notebooks and software repositories). We shall demonstrate some simple methodologies. Can the user access summary data or visualizations of the dataset? It may be that the user is interested in some event, feature or threshold within the dataset; mechanisms need to be provided to allow a user to browse the data (or at least a summary of the data in the most appropriate form be it a plot, table, video etc) prior to making the decision to download or request data. A framework should be flexible enough to allow several methods of visualization. Can datasets be compared and or integrated? By allowing the inclusion of open, 3rd party, standards compliant utilities (e.g. Open Geo-Spatial Consortium WxS clients) into the framework, the utility of a data access system can be made more valuable. Is accessing the actual data straightforward? The process of accessing the data should follow naturally from the data discovery and browsing stages. The user should be made aware of terms and conditions of access. Access restrictions (if applicable) and security should be made as unobtrusive as possible. How is user feedback and comment monitored and acted upon? In general these systems exist to serve science communities, appropriate notice and acknowledgement of their needs and requirements must be taken into account when designing and developing these systems if they are to be of continued use in the future.

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.

OWLing Clinical Data Repositories With the Ontology Web Language

PubMed Central

Pastor, Xavier; Lozano, Esther

2014-01-01

Background The health sciences are based upon information. Clinical information is usually stored and managed by physicians with precarious tools, such as spreadsheets. The biomedical domain is more complex than other domains that have adopted information and communication technologies as pervasive business tools. Moreover, medicine continuously changes its corpus of knowledge because of new discoveries and the rearrangements in the relationships among concepts. This scenario makes it especially difficult to offer good tools to answer the professional needs of researchers and constitutes a barrier that needs innovation to discover useful solutions. Objective The objective was to design and implement a framework for the development of clinical data repositories, capable of facing the continuous change in the biomedicine domain and minimizing the technical knowledge required from final users. Methods We combined knowledge management tools and methodologies with relational technology. We present an ontology-based approach that is flexible and efficient for dealing with complexity and change, integrated with a solid relational storage and a Web graphical user interface. Results Onto Clinical Research Forms (OntoCRF) is a framework for the definition, modeling, and instantiation of data repositories. It does not need any database design or programming. All required information to define a new project is explicitly stated in ontologies. Moreover, the user interface is built automatically on the fly as Web pages, whereas data are stored in a generic repository. This allows for immediate deployment and population of the database as well as instant online availability of any modification. Conclusions OntoCRF is a complete framework to build data repositories with a solid relational storage. Driven by ontologies, OntoCRF is more flexible and efficient to deal with complexity and change than traditional systems and does not require very skilled technical people facilitating the engineering of clinical software systems. PMID:25599697

OWLing Clinical Data Repositories With the Ontology Web Language.

PubMed

Lozano-Rubí, Raimundo; Pastor, Xavier; Lozano, Esther

2014-08-01

The health sciences are based upon information. Clinical information is usually stored and managed by physicians with precarious tools, such as spreadsheets. The biomedical domain is more complex than other domains that have adopted information and communication technologies as pervasive business tools. Moreover, medicine continuously changes its corpus of knowledge because of new discoveries and the rearrangements in the relationships among concepts. This scenario makes it especially difficult to offer good tools to answer the professional needs of researchers and constitutes a barrier that needs innovation to discover useful solutions. The objective was to design and implement a framework for the development of clinical data repositories, capable of facing the continuous change in the biomedicine domain and minimizing the technical knowledge required from final users. We combined knowledge management tools and methodologies with relational technology. We present an ontology-based approach that is flexible and efficient for dealing with complexity and change, integrated with a solid relational storage and a Web graphical user interface. Onto Clinical Research Forms (OntoCRF) is a framework for the definition, modeling, and instantiation of data repositories. It does not need any database design or programming. All required information to define a new project is explicitly stated in ontologies. Moreover, the user interface is built automatically on the fly as Web pages, whereas data are stored in a generic repository. This allows for immediate deployment and population of the database as well as instant online availability of any modification. OntoCRF is a complete framework to build data repositories with a solid relational storage. Driven by ontologies, OntoCRF is more flexible and efficient to deal with complexity and change than traditional systems and does not require very skilled technical people facilitating the engineering of clinical software systems.

The Effect of Rules and Discovery in the Retention and Retrieval of Braille Inkprint Letter Pairs.

ERIC Educational Resources Information Center

Nagengast, Daniel L.; And Others

The effects of rule knowledge were investigated using Braille inkprint pairs. Both recognition and recall were studied in three groups of subjects: rule knowledge, rule discovery, and no rule. Two hypotheses were tested: (1) that the group exposed to the rule would score better than would a discovery group and a control group; and (2) that all…

Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

PubMed

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.

78 FR 35812 - Revisions to Procedural Rules

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-14

... generally id. at 5-11. Within this framework, the Postal Service offers alternatives for reforming discovery processes in N-Cases. Id. at 12- 20. These alternatives include Commission-led discovery, as opposed to participant-led discovery; limits on the number of interrogatories; and clearer and stricter boundaries for...

PSSMSearch: a server for modeling, visualization, proteome-wide discovery and annotation of protein motif specificity determinants.

PubMed

Krystkowiak, Izabella; Manguy, Jean; Davey, Norman E

2018-06-05

There is a pressing need for in silico tools that can aid in the identification of the complete repertoire of protein binding (SLiMs, MoRFs, miniMotifs) and modification (moiety attachment/removal, isomerization, cleavage) motifs. We have created PSSMSearch, an interactive web-based tool for rapid statistical modeling, visualization, discovery and annotation of protein motif specificity determinants to discover novel motifs in a proteome-wide manner. PSSMSearch analyses proteomes for regions with significant similarity to a motif specificity determinant model built from a set of aligned motif-containing peptides. Multiple scoring methods are available to build a position-specific scoring matrix (PSSM) describing the motif specificity determinant model. This model can then be modified by a user to add prior knowledge of specificity determinants through an interactive PSSM heatmap. PSSMSearch includes a statistical framework to calculate the significance of specificity determinant model matches against a proteome of interest. PSSMSearch also includes the SLiMSearch framework's annotation, motif functional analysis and filtering tools to highlight relevant discriminatory information. Additional tools to annotate statistically significant shared keywords and GO terms, or experimental evidence of interaction with a motif-recognizing protein have been added. Finally, PSSM-based conservation metrics have been created for taxonomic range analyses. The PSSMSearch web server is available at http://slim.ucd.ie/pssmsearch/.

Framing of scientific knowledge as a new category of health care research.

PubMed

Salvador-Carulla, Luis; Fernandez, Ana; Madden, Rosamond; Lukersmith, Sue; Colagiuri, Ruth; Torkfar, Ghazal; Sturmberg, Joachim

2014-12-01

The new area of health system research requires a revision of the taxonomy of scientific knowledge that may facilitate a better understanding and representation of complex health phenomena in research discovery, corroboration and implementation. A position paper by an expert group following and iterative approach. 'Scientific evidence' should be differentiated from 'elicited knowledge' of experts and users, and this latter typology should be described beyond the traditional qualitative framework. Within this context 'framing of scientific knowledge' (FSK) is defined as a group of studies of prior expert knowledge specifically aimed at generating formal scientific frames. To be distinguished from other unstructured frames, FSK must be explicit, standardized, based on the available evidence, agreed by a group of experts and subdued to the principles of commensurability, transparency for corroboration and transferability that characterize scientific research. A preliminary typology of scientific framing studies is presented. This typology includes, among others, health declarations, position papers, expert-based clinical guides, conceptual maps, classifications, expert-driven health atlases and expert-driven studies of costs and burden of illness. This grouping of expert-based studies constitutes a different kind of scientific knowledge and should be clearly differentiated from 'evidence' gathered from experimental and observational studies in health system research. © 2014 John Wiley & Sons, Ltd.

A Planetary Defense Gateway for Smart Discovery of relevant Information for Decision Support

NASA Technical Reports Server (NTRS)

Bambacus, Myra; Yang, Chaowei Phil; Leung, Ronald Y.; Barbee, Brent; Nuth, Joseph A.; Seery, Bernard; Jiang, Yongyao; Qin, Han; Li, Yun; Yu, Manzhu;

Concept Formation in Scientific Knowledge Discovery from a Constructivist View

NASA Astrophysics Data System (ADS)

Peng, Wei; Gero, John S.

The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called “first-person” knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer’s first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature review about how people develop their knowledge is carried out through a constructivist view. A hydrological modeling scenario is presented to elucidate the approach.

Concept Formation in Scientific Knowledge Discovery from a Constructivist View

NASA Astrophysics Data System (ADS)

Peng, Wei; Gero, John S.

The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called "first-person" knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer's first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature review about how people develop their knowledge is carried out through a constructivist view. A hydrological modeling scenario is presented to elucidate the approach.

Knowledge Discovery and Data Mining: An Overview

NASA Technical Reports Server (NTRS)

Fayyad, U.

1995-01-01

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 4 2014-01-01 2014-01-01 false Discovery depositions. 263.53 Section 263.53... Discovery depositions. (a) In general. In addition to the discovery permitted in subpart A of this part, limited discovery by means of depositions shall be allowed for individuals with knowledge of facts...

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 4 2012-01-01 2012-01-01 false Discovery depositions. 263.53 Section 263.53... Discovery depositions. (a) In general. In addition to the discovery permitted in subpart A of this part, limited discovery by means of depositions shall be allowed for individuals with knowledge of facts...

Anticoagulant factor V: factors affecting the integration of novel scientific discoveries into the broader framework.

PubMed

LaBonte, Michelle L

2014-09-01

Since its initial discovery in the 1940s, factor V has long been viewed as an important procoagulant protein in the coagulation cascade. However, in the later part of the 20th century, two different scientists proposed novel anticoagulant roles for factor V. Philip Majerus proposed the first anticoagulant function for factor V in 1983, yet ultimately it was not widely accepted by the broader scientific community. In contrast, Björn Dahlbäck proposed a different anticoagulant role for factor V in 1994. While this role was initially contested, it was ultimately accepted and integrated into the scientific framework. In this paper, I present a detailed historical account of these two anticoagulant discoveries and propose three key reasons why Dahlbäck's anticoagulant role for factor V was accepted whereas Majerus' proposed role was largely overlooked. Perhaps most importantly, Dahlbäck's proposed anticoagulant role was of great clinical interest because the discovery involved the study of an important subset of patients with thrombophilia. Soon after Dahlbäck's 1994 work, this patient population was shown to possess the factor V Leiden mutation. Also key in the ultimate acceptance of the second proposed anticoagulant role was the persistence of the scientist who made the discovery and the interest in and ability of others to replicate and reinforce this work. This analysis of two different yet similar discoveries sheds light on factors that play an important role in how new discoveries are incorporated into the existing scientific framework. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

Global OpenSearch

NASA Astrophysics Data System (ADS)

Newman, D. J.; Mitchell, A. E.

2015-12-01

At AGU 2014, NASA EOSDIS demonstrated a case-study of an OpenSearch framework for Earth science data discovery. That framework leverages the IDN and CWIC OpenSearch API implementations to provide seamless discovery of data through the 'two-step' discovery process as outlined by the Federation for Earth Sciences (ESIP) OpenSearch Best Practices. But how would an Earth Scientist leverage this framework and what are the benefits? Using a client that understands the OpenSearch specification and, for further clarity, the various best practices and extensions, a scientist can discovery a plethora of data not normally accessible either by traditional methods (NASA Earth Data Search, Reverb, etc) or direct methods (going to the source of the data) We will demonstrate, via the CWICSmart web client, how an earth scientist can access regional data on a regional phenomena in a uniform and aggregated manner. We will demonstrate how an earth scientist can 'globalize' their discovery. You want to find local data on 'sea surface temperature of the Indian Ocean'? We can help you with that. 'European meteorological data'? Yes. 'Brazilian rainforest satellite imagery'? That too. CWIC allows you to get earth science data in a uniform fashion from a large number of disparate, world-wide agencies. This is what we mean by Global OpenSearch.

Linking Automated Data Analysis and Visualization with Applications in Developmental Biology and High-Energy Physics

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

Ruebel, Oliver

2009-11-20

Knowledge discovery from large and complex collections of today's scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research coveredmore » in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics.Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of high-dimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation becomes possible. The analysis framework MATLAB and the visualization have been integrated, making advanced analysis tools accessible to biologist and enabling bioinformatic researchers to directly integrate their analysis with the visualization. Laser wakefield particle accelerators (LWFAs) promise to be a new compact source of high-energy particles and radiation, with wide applications ranging from medicine to physics. To gain insight into the complex physical processes of particle acceleration, physicists model LWFAs computationally. The datasets produced by LWFA simulations are (i) extremely large, (ii) of varying spatial and temporal resolution, (iii) heterogeneous, and (iv) high-dimensional, making analysis and knowledge discovery from complex LWFA simulation data a challenging task. To address these challenges this thesis describes the integration of the visualization system VisIt and the state-of-the-art index/query system FastBit, enabling interactive visual exploration of extremely large three-dimensional particle datasets. Researchers are especially interested in beams of high-energy particles formed during the course of a simulation. This thesis describes novel methods for automatic detection and analysis of particle beams enabling a more accurate and efficient data analysis process. By integrating these automated analysis methods with visualization, this research enables more accurate, efficient, and effective analysis of LWFA simulation data than previously possible.« less

Data Mining.

ERIC Educational Resources Information Center

Benoit, Gerald

2002-01-01

Discusses data mining (DM) and knowledge discovery in databases (KDD), taking the view that KDD is the larger view of the entire process, with DM emphasizing the cleaning, warehousing, mining, and visualization of knowledge discovery in databases. Highlights include algorithms; users; the Internet; text mining; and information extraction.…

Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data.

PubMed

Erdoğdu, Utku; Tan, Mehmet; Alhajj, Reda; Polat, Faruk; Rokne, Jon; Demetrick, Douglas

2013-01-01

The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framework could successfully complement the actual sample generation from real cases. This argument is validated in this paper by describing a framework that integrates multiple models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a highly demanding area that has not received attention. The three perspectives employed in the process are based on models that are not closely related. The independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The first model is based on the Probabilistic Boolean Network (PBN) representation of the gene regulatory network underlying the given gene expression data. The second model integrates Hierarchical Markov Model (HIMM) and the third model employs a genetic algorithm in the process. Each model learns as much as possible characteristics of the domain being analysed and tries to incorporate the learned characteristics in generating new samples. In other words, the models base their analysis on domain knowledge implicitly present in the data itself. The developed framework has been extensively tested by checking how the new samples complement the original samples. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.

A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data: An Approach for Building Effective Clinical Decision-Making System.

PubMed

Jane, Nancy Yesudhas; Nehemiah, Khanna Harichandran; Arputharaj, Kannan

2016-01-01

Clinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging. This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification. In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier. For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset. The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Resource Discovery within the Networked "Hybrid" Library.

ERIC Educational Resources Information Center

Leigh, Sally-Anne

This paper focuses on the development, adoption, and integration of resource discovery, knowledge management, and/or knowledge sharing interfaces such as interactive portals, and the use of the library's World Wide Web presence to increase the availability and usability of information services. The introduction addresses changes in library…

Virtual physiological human: training challenges.

PubMed

Lawford, Patricia V; Narracott, Andrew V; McCormack, Keith; Bisbal, Jesus; Martin, Carlos; Bijnens, Bart; Brook, Bindi; Zachariou, Margarita; Freixa, Jordi Villà I; Kohl, Peter; Fletcher, Katherine; Diaz-Zuccarini, Vanessa

2010-06-28

The virtual physiological human (VPH) initiative encompasses a wide range of activities, including structural and functional imaging, data mining, knowledge discovery tool and database development, biomedical modelling, simulation and visualization. The VPH community is developing from a multitude of relatively focused, but disparate, research endeavours into an integrated effort to bring together, develop and translate emerging technologies for application, from academia to industry and medicine. This process initially builds on the evolution of multi-disciplinary interactions and abilities, but addressing the challenges associated with the implementation of the VPH will require, in the very near future, a translation of quantitative changes into a new quality of highly trained multi-disciplinary personnel. Current strategies for undergraduate and on-the-job training may soon prove insufficient for this. The European Commission seventh framework VPH network of excellence is exploring this emerging need, and is developing a framework of novel training initiatives to address the predicted shortfall in suitably skilled VPH-aware professionals. This paper reports first steps in the implementation of a coherent VPH training portfolio.

Metadata management and semantics in microarray repositories.

PubMed

Kocabaş, F; Can, T; Baykal, N

2011-12-01

The number of microarray and other high-throughput experiments on primary repositories keeps increasing as do the size and complexity of the results in response to biomedical investigations. Initiatives have been started on standardization of content, object model, exchange format and ontology. However, there are backlogs and inability to exchange data between microarray repositories, which indicate that there is a great need for a standard format and data management. We have introduced a metadata framework that includes a metadata card and semantic nets that make experimental results visible, understandable and usable. These are encoded in syntax encoding schemes and represented in RDF (Resource Description Frame-word), can be integrated with other metadata cards and semantic nets, and can be exchanged, shared and queried. We demonstrated the performance and potential benefits through a case study on a selected microarray repository. We concluded that the backlogs can be reduced and that exchange of information and asking of knowledge discovery questions can become possible with the use of this metadata framework.

Development and use of Ontologies Inside the Neuroscience Information Framework: A Practical Approach

PubMed Central

Imam, Fahim T.; Larson, Stephen D.; Bandrowski, Anita; Grethe, Jeffery S.; Gupta, Amarnath; Martone, Maryann E.

2012-01-01

An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF’s production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies. PMID:22737162

Integrating computational methods to retrofit enzymes to synthetic pathways.

PubMed

Brunk, Elizabeth; Neri, Marilisa; Tavernelli, Ivano; Hatzimanikatis, Vassily; Rothlisberger, Ursula

2012-02-01

Microbial production of desired compounds provides an efficient framework for the development of renewable energy resources. To be competitive to traditional chemistry, one requirement is to utilize the full capacity of the microorganism to produce target compounds with high yields and turnover rates. We use integrated computational methods to generate and quantify the performance of novel biosynthetic routes that contain highly optimized catalysts. Engineering a novel reaction pathway entails addressing feasibility on multiple levels, which involves handling the complexity of large-scale biochemical networks while respecting the critical chemical phenomena at the atomistic scale. To pursue this multi-layer challenge, our strategy merges knowledge-based metabolic engineering methods with computational chemistry methods. By bridging multiple disciplines, we provide an integral computational framework that could accelerate the discovery and implementation of novel biosynthetic production routes. Using this approach, we have identified and optimized a novel biosynthetic route for the production of 3HP from pyruvate. Copyright © 2011 Wiley Periodicals, Inc.

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Form-Focused Discovery Activities in English Classes

ERIC Educational Resources Information Center

Ogeyik, Muhlise Cosgun

2011-01-01

Form-focused discovery activities allow language learners to grasp various aspects of a target language by contributing implicit knowledge by using discovered explicit knowledge. Moreover, such activities can assist learners to perceive and discover the features of their language input. In foreign language teaching environments, they can be used…

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

PubMed Central

Chung, Yongchul G.; Gómez-Gualdrón, Diego A.; Li, Peng; Leperi, Karson T.; Deria, Pravas; Zhang, Hongda; Vermeulen, Nicolaas A.; Stoddart, J. Fraser; You, Fengqi; Hupp, Joseph T.; Farha, Omar K.; Snurr, Randall Q.

2016-01-01

Discovery of new adsorbent materials with a high CO2 working capacity could help reduce CO2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO2 capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO2 working capacity and a high CO2/H2 selectivity. One of the synthesized MOFs shows a higher CO2 working capacity than any MOF reported in the literature under the operating conditions investigated here. PMID:27757420

75 FR 66766 - NIAID Blue Ribbon Panel Meeting on Adjuvant Discovery and Development

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-29

..., identifies gaps in knowledge and capabilities, and defines NIAID's goals for the continued discovery... DEPARTMENT OF HEALTH AND HUMAN SERVICES NIAID Blue Ribbon Panel Meeting on Adjuvant Discovery and... agenda for the discovery, development and clinical evaluation of adjuvants for use with preventive...

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 3 2011-01-01 2011-01-01 false Discovery depositions. 263.53 Section 263.53... depositions. (a) In general. In addition to the discovery permitted in subpart A of this part, limited discovery by means of depositions shall be allowed for individuals with knowledge of facts material to the...

12 CFR 19.170 - Discovery depositions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 1 2010-01-01 2010-01-01 false Discovery depositions. 19.170 Section 19.170... PROCEDURE Discovery Depositions and Subpoenas § 19.170 Discovery depositions. (a) General rule. In any... deposition of an expert, or of a person, including another party, who has direct knowledge of matters that...

12 CFR 19.170 - Discovery depositions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 1 2011-01-01 2011-01-01 false Discovery depositions. 19.170 Section 19.170... PROCEDURE Discovery Depositions and Subpoenas § 19.170 Discovery depositions. (a) General rule. In any... deposition of an expert, or of a person, including another party, who has direct knowledge of matters that...

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 3 2010-01-01 2010-01-01 false Discovery depositions. 263.53 Section 263.53... depositions. (a) In general. In addition to the discovery permitted in subpart A of this part, limited discovery by means of depositions shall be allowed for individuals with knowledge of facts material to the...

BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

PubMed Central

2011-01-01

We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at http://www.biograph.be. PMID:21696594

Earth's earliest biosphere-a proposal to develop a collection of curated archean geologic reference materials

NASA Technical Reports Server (NTRS)

Lindsay, John F.; McKay, David S.; Allen, Carlton C.

2003-01-01

The discovery of evidence indicative of life in a Martian meteorite has led to an increase in interest in astrobiology. As a result of this discovery, and the ensuing controversy, it has become apparent that our knowledge of the early development of life on Earth is limited. Archean stratigraphic successions containing evidence of Earth's early biosphere are well preserved in the Pilbara Craton of Western Australia. The craton includes part of a protocontinent consisting of granitoid complexes that were emplaced into, and overlain by, a 3.51-2.94 Ga volcanigenic carapace - the Pilbara Supergroup. The craton is overlain by younger supracrustal basins that form a time series recording Earth history from approximately 2.8 Ga to approximately 1.9 Ga. It is proposed that a well-documented suite of these ancient rocks be collected as reference material for Archean and astrobiological research. All samples would be collected in a well-defined geological context in order to build a framework to test models for the early evolution of life on Earth and to develop protocols for the search for life on other planets.

Targeted drug discovery and development, from molecular signaling to the global market: an educational program at New York University, 5-year metrics

PubMed Central

Lee, Gloria; Plaksin, Joseph; Ramasamy, Ravichandran; Gold-von Simson, Gabrielle

2018-01-01

Drug discovery and development (DDD) is a collaborative, dynamic process of great interest to researchers, but an area where there is a lack of formal training. The Drug Development Educational Program (DDEP) at New York University was created in 2012 to stimulate an improved, multidisciplinary DDD workforce by educating early stage scientists as well as a variety of other like-minded students. The first course of the program emphasizes post-compounding aspects of DDD; the second course focuses on molecular signaling pathways. In five years, 196 students (candidates for PhD, MD, Master’s degree, and post-doctoral MD/PhD) from different schools (Medicine, Biomedical Sciences, Dentistry, Engineering, Business, and Education) completed the course(s). Pre/post surveys demonstrate knowledge gain across all course topics. 26 students were granted career development awards (73% women, 23% underrepresented minorities). Some graduates of their respective degree-granting/post-doctoral programs embarked on DDD related careers. This program serves as a framework for other academic institutions to develop compatible programs designed to train a more informed DDD workforce. PMID:29657854

“Drivers” of Translational Cancer Epidemiology in the 21st Century: Needs and Opportunities

PubMed Central

Lam, Tram Kim; Spitz, Margaret; Schully, Sheri D.; Khoury, Muin J.

2012-01-01

Cancer epidemiology is at the cusp of a paradigm shift--propelled by an urgent need to accelerate the pace of translating scientific discoveries into healthcare and population health benefits. As part of a strategic planning process for cancer epidemiologic research, the Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI) is leading a “longitudinal” meeting with members of the research community to engage in an on-going dialogue to help shape and invigorate the field. Here, we review a translational framework influenced by “drivers” that we believe have begun guiding cancer epidemiology towards translation in the past few years and are most likely to drive the field further in the next decade. The drivers include: (1) collaboration and team science; (2) technology; (3) multi-level analyses and interventions; and (4) knowledge integration from basic, clinical and population sciences. Using the global prevention of cervical cancer as an example of a public health endeavor to anchor the conversation, we discuss how these drivers can guide epidemiology from discovery to population health impact, along the translational research continuum. PMID:23322363

Towards a Web-Enabled Geovisualization and Analytics Platform for the Energy and Water Nexus

NASA Astrophysics Data System (ADS)

Sanyal, J.; Chandola, V.; Sorokine, A.; Allen, M.; Berres, A.; Pang, H.; Karthik, R.; Nugent, P.; McManamay, R.; Stewart, R.; Bhaduri, B. L.

2017-12-01

Interactive data analytics are playing an increasingly vital role in the generation of new, critical insights regarding the complex dynamics of the energy/water nexus (EWN) and its interactions with climate variability and change. Integration of impacts, adaptation, and vulnerability (IAV) science with emerging, and increasingly critical, data science capabilities offers a promising potential to meet the needs of the EWN community. To enable the exploration of pertinent research questions, a web-based geospatial visualization platform is being built that integrates a data analysis toolbox with advanced data fusion and data visualization capabilities to create a knowledge discovery framework for the EWN. The system, when fully built out, will offer several geospatial visualization capabilities including statistical visual analytics, clustering, principal-component analysis, dynamic time warping, support uncertainty visualization and the exploration of data provenance, as well as support machine learning discoveries to render diverse types of geospatial data and facilitate interactive analysis. Key components in the system architecture includes NASA's WebWorldWind, the Globus toolkit, postgresql, as well as other custom built software modules.

VGLUTs in Peripheral Neurons and the Spinal Cord: Time for a Review

PubMed Central

Brumovsky, Pablo R.

2013-01-01

Vesicular glutamate transporters (VGLUTs) are key molecules for the incorporation of glutamate in synaptic vesicles across the nervous system, and since their discovery in the early 1990s, research on these transporters has been intense and productive. This review will focus on several aspects of VGLUTs research on neurons in the periphery and the spinal cord. Firstly, it will begin with a historical account on the evolution of the morphological analysis of glutamatergic systems and the pivotal role played by the discovery of VGLUTs. Secondly, and in order to provide an appropriate framework, there will be a synthetic description of the neuroanatomy and neurochemistry of peripheral neurons and the spinal cord. This will be followed by a succinct description of the current knowledge on the expression of VGLUTs in peripheral sensory and autonomic neurons and neurons in the spinal cord. Finally, this review will address the modulation of VGLUTs expression after nerve and tissue insult, their physiological relevance in relation to sensation, pain, and neuroprotection, and their potential pharmacological usefulness. PMID:24349795

Essential Annotation Schema for Ecology (EASE)-A framework supporting the efficient data annotation and faceted navigation in ecology.

PubMed

Pfaff, Claas-Thido; Eichenberg, David; Liebergesell, Mario; König-Ries, Birgitta; Wirth, Christian

2017-01-01

Ecology has become a data intensive science over the last decades which often relies on the reuse of data in cross-experimental analyses. However, finding data which qualifies for the reuse in a specific context can be challenging. It requires good quality metadata and annotations as well as efficient search strategies. To date, full text search (often on the metadata only) is the most widely used search strategy although it is known to be inaccurate. Faceted navigation is providing a filter mechanism which is based on fine granular metadata, categorizing search objects along numeric and categorical parameters relevant for their discovery. Selecting from these parameters during a full text search creates a system of filters which allows to refine and improve the results towards more relevance. We developed a framework for the efficient annotation and faceted navigation in ecology. It consists of an XML schema for storing the annotation of search objects and is accompanied by a vocabulary focused on ecology to support the annotation process. The framework consolidates ideas which originate from widely accepted metadata standards, textbooks, scientific literature, and vocabularies as well as from expert knowledge contributed by researchers from ecology and adjacent disciplines.

Medical knowledge discovery and management.

PubMed

Prior, Fred

2009-05-01

Although the volume of medical information is growing rapidly, the ability to rapidly convert this data into "actionable insights" and new medical knowledge is lagging far behind. The first step in the knowledge discovery process is data management and integration, which logically can be accomplished through the application of data warehouse technologies. A key insight that arises from efforts in biosurveillance and the global scope of military medicine is that information must be integrated over both time (longitudinal health records) and space (spatial localization of health-related events). Once data are compiled and integrated it is essential to encode the semantics and relationships among data elements through the use of ontologies and semantic web technologies to convert data into knowledge. Medical images form a special class of health-related information. Traditionally knowledge has been extracted from images by human observation and encoded via controlled terminologies. This approach is rapidly being replaced by quantitative analyses that more reliably support knowledge extraction. The goals of knowledge discovery are the improvement of both the timeliness and accuracy of medical decision making and the identification of new procedures and therapies.

Taking stock of current societal, political and academic stakeholders in the Canadian healthcare knowledge translation agenda

PubMed Central

Newton, Mandi S; Scott-Findlay, Shannon

2007-01-01

Background In the past 15 years, knowledge translation in healthcare has emerged as a multifaceted and complex agenda. Theoretical and polemical discussions, the development of a science to study and measure the effects of translating research evidence into healthcare, and the role of key stakeholders including academe, healthcare decision-makers, the public, and government funding bodies have brought scholarly, organizational, social, and political dimensions to the agenda. Objective This paper discusses the current knowledge translation agenda in Canadian healthcare and how elements in this agenda shape the discovery and translation of health knowledge. Discussion The current knowledge translation agenda in Canadian healthcare involves the influence of values, priorities, and people; stakes which greatly shape the discovery of research knowledge and how it is or is not instituted in healthcare delivery. As this agenda continues to take shape and direction, ensuring that it is accountable for its influences is essential and should be at the forefront of concern to the Canadian public and healthcare community. This transparency will allow for scrutiny, debate, and improvements in health knowledge discovery and health services delivery. PMID:17916256

Concept of operations for knowledge discovery from Big Data across enterprise data warehouses

NASA Astrophysics Data System (ADS)

Sukumar, Sreenivas R.; Olama, Mohammed M.; McNair, Allen W.; Nutaro, James J.

2013-05-01

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Options that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.

Pierre Bourdieu's Theory of Practice offers nurses a framework to uncover embodied knowledge of patients living with disabilities or illnesses: A discussion paper.

PubMed

Oerther, Sarah; Oerther, Daniel B

2018-04-01

To discuss how Bourdieu's theory of practice can be used by nurse researchers to better uncover the embodied knowledge of patients living with disability and illness. Bourdieu's theory of practice has been used in social and healthcare researches. This theory emphasizes that an individual's everyday practices are not always explicit and mediated by language, but instead an individual's everyday practices are often are tacit and embodied. Discussion paper. Ovid MEDLINE, CINAHL and SCOPUS were searched for concepts from Bourdieu's theory that was used to understand embodied knowledge of patients living with disability and illness. The literature search included articles from 2003 - 2017. Nurse researchers should use Bourdieu's theory of practice to uncover the embodied knowledge of patients living with disability and illness, and nurse researchers should translate these discoveries into policy recommendations and improved evidence-based best practice. The practice of nursing should incorporate an understanding of embodied knowledge to support disabled and ill patients as these patients modify "everyday practices" in the light of their disabilities and illnesses. Bourdieu's theory enriches nursing because the theory allows for consideration of both the objective and the subjective through the conceptualization of capital, habitus and field. Uncovering individuals embodied knowledge is critical to implement best practices that assist patients as they adapt to bodily changes during disability and illness. © 2017 John Wiley & Sons Ltd.

Cosmology Without Finality

NASA Astrophysics Data System (ADS)

Mahootian, F.

2009-12-01

The rapid convergence of advancing sensor technology, computational power, and knowledge discovery techniques over the past decade has brought unprecedented volumes of astronomical data together with unprecedented capabilities of data assimilation and analysis. A key result is that a new, data-driven "observational-inductive'' framework for scientific inquiry is taking shape and proving viable. The anticipated rise in data flow and processing power will have profound effects, e.g., confirmations and disconfirmations of existing theoretical claims both for and against the big bang model. But beyond enabling new discoveries can new data-driven frameworks of scientific inquiry reshape the epistemic ideals of science? The history of physics offers a comparison. The Bohr-Einstein debate over the "completeness'' of quantum mechanics centered on a question of ideals: what counts as science? We briefly examine lessons from that episode and pose questions about their applicability to cosmology. If the history of 20th century physics is any indication, the abandonment of absolutes (e.g., space, time, simultaneity, continuity, determinacy) can produce fundamental changes in understanding. The classical ideal of science, operative in both physics and cosmology, descends from the European Enlightenment. This ideal has for over 200 years guided science to seek the ultimate order of nature, to pursue the absolute theory, the "theory of everything.'' But now that we have new models of scientific inquiry powered by new technologies and driven more by data than by theory, it is time, finally, to relinquish dreams of a "final'' theory.

The center for causal discovery of biomedical knowledge from big data.

PubMed

Cooper, Gregory F; Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-11-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.All rights reserved. For Permissions, please email: journals.permissions@oup.com.

How does non-formal marine education affect student attitude and knowledge? A case study using SCDNR's Discovery program

NASA Astrophysics Data System (ADS)

McGovern, Mary Francis

Non-formal environmental education provides students the opportunity to learn in ways that would not be possible in a traditional classroom setting. Outdoor learning allows students to make connections to their environment and helps to foster an appreciation for nature. This type of education can be interdisciplinary---students not only develop skills in science, but also in mathematics, social studies, technology, and critical thinking. This case study focuses on a non-formal marine education program, the South Carolina Department of Natural Resources' (SCDNR) Discovery vessel based program. The Discovery curriculum was evaluated to determine impact on student knowledge about and attitude toward the estuary. Students from two South Carolina coastal counties who attended the boat program during fall 2014 were asked to complete a brief survey before, immediately after, and two weeks following the program. The results of this study indicate that both student knowledge about and attitude significantly improved after completion of the Discovery vessel based program. Knowledge and attitude scores demonstrated a positive correlation.

Construction of an ortholog database using the semantic web technology for integrative analysis of genomic data.

PubMed

Chiba, Hirokazu; Nishide, Hiroyo; Uchiyama, Ikuo

2015-01-01

Recently, various types of biological data, including genomic sequences, have been rapidly accumulating. To discover biological knowledge from such growing heterogeneous data, a flexible framework for data integration is necessary. Ortholog information is a central resource for interlinking corresponding genes among different organisms, and the Semantic Web provides a key technology for the flexible integration of heterogeneous data. We have constructed an ortholog database using the Semantic Web technology, aiming at the integration of numerous genomic data and various types of biological information. To formalize the structure of the ortholog information in the Semantic Web, we have constructed the Ortholog Ontology (OrthO). While the OrthO is a compact ontology for general use, it is designed to be extended to the description of database-specific concepts. On the basis of OrthO, we described the ortholog information from our Microbial Genome Database for Comparative Analysis (MBGD) in the form of Resource Description Framework (RDF) and made it available through the SPARQL endpoint, which accepts arbitrary queries specified by users. In this framework based on the OrthO, the biological data of different organisms can be integrated using the ortholog information as a hub. Besides, the ortholog information from different data sources can be compared with each other using the OrthO as a shared ontology. Here we show some examples demonstrating that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology. Thus, the ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis.

A data-driven, knowledge-based approach to biomarker discovery: application to circulating microRNA markers of colorectal cancer prognosis.

PubMed

Vafaee, Fatemeh; Diakos, Connie; Kirschner, Michaela B; Reid, Glen; Michael, Michael Z; Horvath, Lisa G; Alinejad-Rokny, Hamid; Cheng, Zhangkai Jason; Kuncic, Zdenka; Clarke, Stephen

2018-01-01

Recent advances in high-throughput technologies have provided an unprecedented opportunity to identify molecular markers of disease processes. This plethora of complex-omics data has simultaneously complicated the problem of extracting meaningful molecular signatures and opened up new opportunities for more sophisticated integrative and holistic approaches. In this era, effective integration of data-driven and knowledge-based approaches for biomarker identification has been recognised as key to improving the identification of high-performance biomarkers, and necessary for translational applications. Here, we have evaluated the role of circulating microRNA as a means of predicting the prognosis of patients with colorectal cancer, which is the second leading cause of cancer-related death worldwide. We have developed a multi-objective optimisation method that effectively integrates a data-driven approach with the knowledge obtained from the microRNA-mediated regulatory network to identify robust plasma microRNA signatures which are reliable in terms of predictive power as well as functional relevance. The proposed multi-objective framework has the capacity to adjust for conflicting biomarker objectives and to incorporate heterogeneous information facilitating systems approaches to biomarker discovery. We have found a prognostic signature of colorectal cancer comprising 11 circulating microRNAs. The identified signature predicts the patients' survival outcome and targets pathways underlying colorectal cancer progression. The altered expression of the identified microRNAs was confirmed in an independent public data set of plasma samples of patients in early stage vs advanced colorectal cancer. Furthermore, the generality of the proposed method was demonstrated across three publicly available miRNA data sets associated with biomarker studies in other diseases.

Evidence-based medicine: is it a bridge too far?

PubMed

Fernandez, Ana; Sturmberg, Joachim; Lukersmith, Sue; Madden, Rosamond; Torkfar, Ghazal; Colagiuri, Ruth; Salvador-Carulla, Luis

2015-11-06

This paper aims to describe the contextual factors that gave rise to evidence-based medicine (EBM), as well as its controversies and limitations in the current health context. Our analysis utilizes two frameworks: (1) a complex adaptive view of health that sees both health and healthcare as non-linear phenomena emerging from their different components; and (2) the unified approach to the philosophy of science that provides a new background for understanding the differences between the phases of discovery, corroboration, and implementation in science. The need for standardization, the development of clinical epidemiology, concerns about the economic sustainability of health systems and increasing numbers of clinical trials, together with the increase in the computer's ability to handle large amounts of data, have paved the way for the development of the EBM movement. It was quickly adopted on the basis of authoritative knowledge rather than evidence of its own capacity to improve the efficiency and equity of health systems. The main problem with the EBM approach is the restricted and simplistic approach to scientific knowledge, which prioritizes internal validity as the major quality of the studies to be included in clinical guidelines. As a corollary, the preferred method for generating evidence is the explanatory randomized controlled trial. This method can be useful in the phase of discovery but is inadequate in the field of implementation, which needs to incorporate additional information including expert knowledge, patients' values and the context. EBM needs to move forward and perceive health and healthcare as a complex interaction, i.e. an interconnected, non-linear phenomenon that may be better analysed using a variety of complexity science techniques.

Constraint-based Data Mining

NASA Astrophysics Data System (ADS)

Boulicaut, Jean-Francois; Jeudy, Baptiste

Knowledge Discovery in Databases (KDD) is a complex interactive process. The promising theoretical framework of inductive databases considers this is essentially a querying process. It is enabled by a query language which can deal either with raw data or patterns which hold in the data. Mining patterns turns to be the so-called inductive query evaluation process for which constraint-based Data Mining techniques have to be designed. An inductive query specifies declaratively the desired constraints and algorithms are used to compute the patterns satisfying the constraints in the data. We survey important results of this active research domain. This chapter emphasizes a real breakthrough for hard problems concerning local pattern mining under various constraints and it points out the current directions of research as well.

Representing Learning With Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.

Crowdsourcing applications for public health.

PubMed

Brabham, Daren C; Ribisl, Kurt M; Kirchner, Thomas R; Bernhardt, Jay M

2014-02-01

Crowdsourcing is an online, distributed, problem-solving, and production model that uses the collective intelligence of networked communities for specific purposes. Although its use has benefited many sectors of society, it has yet to be fully realized as a method for improving public health. This paper defines the core components of crowdsourcing and proposes a framework for understanding the potential utility of crowdsourcing in the domain of public health. Four discrete crowdsourcing approaches are described (knowledge discovery and management; distributed human intelligence tasking; broadcast search; and peer-vetted creative production types) and a number of potential applications for crowdsourcing for public health science and practice are enumerated. © 2013 American Journal of Preventive Medicine Published by American Journal of Preventive Medicine All rights reserved.

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

PubMed Central

Lionta, Evanthia; Spyrou, George; Vassilatis, Demetrios K.; Cournia, Zoe

2014-01-01

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process. PMID:25262799

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

PubMed

Dobchev, Dimitar; Karelson, Mati

2016-07-01

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

External Dependencies-Driven Architecture Discovery and Analysis of Implemented Systems

NASA Technical Reports Server (NTRS)

Ganesan, Dharmalingam; Lindvall, Mikael; Ron, Monica

2014-01-01

A method for architecture discovery and analysis of implemented systems (AIS) is disclosed. The premise of the method is that architecture decisions are inspired and influenced by the external entities that the software system makes use of. Examples of such external entities are COTS components, frameworks, and ultimately even the programming language itself and its libraries. Traces of these architecture decisions can thus be found in the implemented software and is manifested in the way software systems use such external entities. While this fact is often ignored in contemporary reverse engineering methods, the AIS method actively leverages and makes use of the dependencies to external entities as a starting point for the architecture discovery. The AIS method is demonstrated using the NASA's Space Network Access System (SNAS). The results show that, with abundant evidence, the method offers reusable and repeatable guidelines for discovering the architecture and locating potential risks (e.g. low testability, decreased performance) that are hidden deep in the implementation. The analysis is conducted by using external dependencies to identify, classify and review a minimal set of key source code files. Given the benefits of analyzing external dependencies as a way to discover architectures, it is argued that external dependencies deserve to be treated as first-class citizens during reverse engineering. The current structure of a knowledge base of external entities and analysis questions with strategies for getting answers is also discussed.

A bioinformatics knowledge discovery in text application for grid computing

PubMed Central

Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

2009-01-01

Background A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. Methods The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. Results A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. Conclusion In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities. PMID:19534749

A bioinformatics knowledge discovery in text application for grid computing.

PubMed

Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

2009-06-16

A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities.

Enhancing Learning Environments through Solution-based Knowledge Discovery Tools: Forecasting for Self-Perpetuating Systemic Reform.

ERIC Educational Resources Information Center

Tsantis, Linda; Castellani, John

2001-01-01

This article explores how knowledge-discovery applications can empower educators with the information they need to provide anticipatory guidance for teaching and learning, forecast school and district needs, and find critical markers for making the best program decisions for children and youth with disabilities. Data mining for schools is…

Application of Knowledge Discovery in Databases Methodologies for Predictive Models for Pregnancy Adverse Events

ERIC Educational Resources Information Center

Taft, Laritza M.

2010-01-01

In its report "To Err is Human", The Institute of Medicine recommended the implementation of internal and external voluntary and mandatory automatic reporting systems to increase detection of adverse events. Knowledge Discovery in Databases (KDD) allows the detection of patterns and trends that would be hidden or less detectable if analyzed by…

Knowledge Discovery Process: Case Study of RNAV Adherence of Radar Track Data

NASA Technical Reports Server (NTRS)

Matthews, Bryan

2018-01-01

This talk is an introduction to the knowledge discovery process, beginning with: identifying the problem, choosing data sources, matching the appropriate machine learning tools, and reviewing the results. The overview will be given in the context of an ongoing study that is assessing RNAV adherence of commercial aircraft in the national airspace.

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

NASA Technical Reports Server (NTRS)

Crichton, Daniel; Srivastava, Sudhir; Johnsey, Donald

2003-01-01

Discovery of disease biomarkers for cancer is a leading focus of early detection. The National Cancer Institute created a network of collaborating institutions focused on the discovery and validation of cancer biomarkers called the Early Detection Research Network (EDRN). Informatics plays a key role in enabling a virtual knowledge environment that provides scientists real time access to distributed data sets located at research institutions across the nation. The distributed and heterogeneous nature of the collaboration makes data sharing across institutions very difficult. EDRN has developed a comprehensive informatics effort focused on developing a national infrastructure enabling seamless access, sharing and discovery of science data resources across all EDRN sites. This paper will discuss the EDRN knowledge system architecture, its objectives and its accomplishments.

Empowering Accelerated Personal, Professional and Scholarly Discovery among Information Seekers: An Educational Vision

ERIC Educational Resources Information Center

Harmon, Glynn

2013-01-01

The term discovery applies herein to the successful outcome of inquiry in which a significant personal, professional or scholarly breakthrough or insight occurs, and which is individually or socially acknowledged as a key contribution to knowledge. Since discoveries culminate at fixed points in time, discoveries can serve as an outcome metric for…

An Unsupervised Anomalous Event Detection and Interactive Analysis Framework for Large-scale Satellite Data

NASA Astrophysics Data System (ADS)

LIU, Q.; Lv, Q.; Klucik, R.; Chen, C.; Gallaher, D. W.; Grant, G.; Shang, L.

2016-12-01

Due to the high volume and complexity of satellite data, computer-aided tools for fast quality assessments and scientific discovery are indispensable for scientists in the era of Big Data. In this work, we have developed a framework for automated anomalous event detection in massive satellite data. The framework consists of a clustering-based anomaly detection algorithm and a cloud-based tool for interactive analysis of detected anomalies. The algorithm is unsupervised and requires no prior knowledge of the data (e.g., expected normal pattern or known anomalies). As such, it works for diverse data sets, and performs well even in the presence of missing and noisy data. The cloud-based tool provides an intuitive mapping interface that allows users to interactively analyze anomalies using multiple features. As a whole, our framework can (1) identify outliers in a spatio-temporal context, (2) recognize and distinguish meaningful anomalous events from individual outliers, (3) rank those events based on "interestingness" (e.g., rareness or total number of outliers) defined by users, and (4) enable interactively query, exploration, and analysis of those anomalous events. In this presentation, we will demonstrate the effectiveness and efficiency of our framework in the application of detecting data quality issues and unusual natural events using two satellite datasets. The techniques and tools developed in this project are applicable for a diverse set of satellite data and will be made publicly available for scientists in early 2017.

Active pharmaceutical ingredient (API) production involving continuous processes--a process system engineering (PSE)-assisted design framework.

PubMed

Cervera-Padrell, Albert E; Skovby, Tommy; Kiil, Søren; Gani, Rafiqul; Gernaey, Krist V

2012-10-01

A systematic framework is proposed for the design of continuous pharmaceutical manufacturing processes. Specifically, the design framework focuses on organic chemistry based, active pharmaceutical ingredient (API) synthetic processes, but could potentially be extended to biocatalytic and fermentation-based products. The method exploits the synergic combination of continuous flow technologies (e.g., microfluidic techniques) and process systems engineering (PSE) methods and tools for faster process design and increased process understanding throughout the whole drug product and process development cycle. The design framework structures the many different and challenging design problems (e.g., solvent selection, reactor design, and design of separation and purification operations), driving the user from the initial drug discovery steps--where process knowledge is very limited--toward the detailed design and analysis. Examples from the literature of PSE methods and tools applied to pharmaceutical process design and novel pharmaceutical production technologies are provided along the text, assisting in the accumulation and interpretation of process knowledge. Different criteria are suggested for the selection of batch and continuous processes so that the whole design results in low capital and operational costs as well as low environmental footprint. The design framework has been applied to the retrofit of an existing batch-wise process used by H. Lundbeck A/S to produce an API: zuclopenthixol. Some of its batch operations were successfully converted into continuous mode, obtaining higher yields that allowed a significant simplification of the whole process. The material and environmental footprint of the process--evaluated through the process mass intensity index, that is, kg of material used per kg of product--was reduced to half of its initial value, with potential for further reduction. The case-study includes reaction steps typically used by the pharmaceutical industry featuring different characteristic reaction times, as well as L-L separation and distillation-based solvent exchange steps, and thus constitutes a good example of how the design framework can be useful to efficiently design novel or already existing API manufacturing processes taking advantage of continuous processes. Copyright © 2012 Elsevier B.V. All rights reserved.

A meta-learning system based on genetic algorithms

NASA Astrophysics Data System (ADS)

Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain

2004-04-01

The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.

'Big Data' Collaboration: Exploring, Recording and Sharing Enterprise Knowledge

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

Sukumar, Sreenivas R; Ferrell, Regina Kay

2013-01-01

As data sources and data size proliferate, knowledge discovery from "Big Data" is starting to pose several challenges. In this paper, we address a specific challenge in the practice of enterprise knowledge management while extracting actionable nuggets from diverse data sources of seemingly-related information. In particular, we address the challenge of archiving knowledge gained through collaboration, dissemination and visualization as part of the data analysis, inference and decision-making lifecycle. We motivate the implementation of an enterprise data-discovery and knowledge recorder tool, called SEEKER based on real world case-study. We demonstrate SEEKER capturing schema and data-element relationships, tracking the data elementsmore » of value based on the queries and the analytical artifacts that are being created by analysts as they use the data. We show how the tool serves as digital record of institutional domain knowledge and a documentation for the evolution of data elements, queries and schemas over time. As a knowledge management service, a tool like SEEKER saves enterprise resources and time by avoiding analytic silos, expediting the process of multi-source data integration and intelligently documenting discoveries from fellow analysts.« less

Operationalizing the Learning Health Care System in an Integrated Delivery System

PubMed Central

Psek, Wayne A.; Stametz, Rebecca A.; Bailey-Davis, Lisa D.; Davis, Daniel; Darer, Jonathan; Faucett, William A.; Henninger, Debra L.; Sellers, Dorothy C.; Gerrity, Gloria

2015-01-01

Introduction: The Learning Health Care System (LHCS) model seeks to utilize sophisticated technologies and competencies to integrate clinical operations, research and patient participation in order to continuously generate knowledge, improve care, and deliver value. Transitioning from concept to practical application of an LHCS presents many challenges but can yield opportunities for continuous improvement. There is limited literature and practical experience available in operationalizing the LHCS in the context of an integrated health system. At Geisinger Health System (GHS) a multi-stakeholder group is undertaking to enhance organizational learning and develop a plan for operationalizing the LHCS system-wide. We present a framework for operationalizing continuous learning across an integrated delivery system and lessons learned through the ongoing planning process. Framework: The framework focuses attention on nine key LHCS operational components: Data and Analytics; People and Partnerships; Patient and Family Engagement; Ethics and Oversight; Evaluation and Methodology; Funding; Organization; Prioritization; and Deliverables. Definitions, key elements and examples for each are presented. The framework is purposefully broad for application across different organizational contexts. Conclusion: A realistic assessment of the culture, resources and capabilities of the organization related to learning is critical to defining the scope of operationalization. Engaging patients in clinical care and discovery, including quality improvement and comparative effectiveness research, requires a defensible ethical framework that undergirds a system of strong but flexible oversight. Leadership support is imperative for advancement of the LHCS model. Findings from our ongoing work within the proposed framework may inform other organizations considering a transition to an LHCS. PMID:25992388

Lowering the barriers to computational modeling of Earth's surface: coupling Jupyter Notebooks with Landlab, HydroShare, and CyberGIS for research and education.

NASA Astrophysics Data System (ADS)

Bandaragoda, C.; Castronova, A. M.; Phuong, J.; Istanbulluoglu, E.; Strauch, R. L.; Nudurupati, S. S.; Tarboton, D. G.; Wang, S. W.; Yin, D.; Barnhart, K. R.; Tucker, G. E.; Hutton, E.; Hobley, D. E. J.; Gasparini, N. M.; Adams, J. M.

2017-12-01

The ability to test hypotheses about hydrology, geomorphology and atmospheric processes is invaluable to research in the era of big data. Although community resources are available, there remain significant educational, logistical and time investment barriers to their use. Knowledge infrastructure is an emerging intellectual framework to understand how people are creating, sharing and distributing knowledge - which has been dramatically transformed by Internet technologies. In addition to the technical and social components in a cyberinfrastructure system, knowledge infrastructure considers educational, institutional, and open source governance components required to advance knowledge. We are designing an infrastructure environment that lowers common barriers to reproducing modeling experiments for earth surface investigation. Landlab is an open-source modeling toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for sharing hydrologic data and models. CyberGIS-Jupyter is an innovative cyberGIS framework for achieving data-intensive, reproducible, and scalable geospatial analytics using the Jupyter Notebook based on ROGER - the first cyberGIS supercomputer, so that models that can be elastically reproduced through cloud computing approaches. Our team of geomorphologists, hydrologists, and computer geoscientists has created a new infrastructure environment that combines these three pieces of software to enable knowledge discovery. Through this novel integration, any user can interactively execute and explore their shared data and model resources. Landlab on HydroShare with CyberGIS-Jupyter supports the modeling continuum from fully developed modelling applications, prototyping new science tools, hands on research demonstrations for training workshops, and classroom applications. Computational geospatial models based on big data and high performance computing can now be more efficiently developed, improved, scaled, and seamlessly reproduced among multidisciplinary users, thereby expanding the active learning curriculum and research opportunities for students in earth surface modeling and informatics.

The Contribution of Conceptual Frameworks to Knowledge Translation Interventions in Physical Therapy

PubMed Central

Gervais, Mathieu-Joël; Hunt, Matthew

2015-01-01

There is growing recognition of the importance of knowledge translation activities in physical therapy to ensure that research findings are integrated into clinical practice, and increasing numbers of knowledge translation interventions are being conducted. Although various frameworks have been developed to guide and facilitate the process of translating knowledge into practice, these tools have been infrequently used in physical therapy knowledge translation studies to date. Knowledge translation in physical therapy implicates multiple stakeholders and environments and involves numerous steps. In light of this complexity, the use of explicit conceptual frameworks by clinicians and researchers conducting knowledge translation interventions is associated with a range of potential benefits. This perspective article argues that such frameworks are important resources to promote the uptake of new evidence in physical therapist practice settings. Four key benefits associated with the use of conceptual frameworks in designing and implementing knowledge translation interventions are identified, and limits related to their use are considered. A sample of 5 conceptual frameworks is evaluated, and how they address common barriers to knowledge translation in physical therapy is assessed. The goal of this analysis is to provide guidance to physical therapists seeking to identify a framework to support the design and implementation of a knowledge translation intervention. Finally, the use of a conceptual framework is illustrated through a case example. Increased use of conceptual frameworks can have a positive impact on the field of knowledge translation in physical therapy and support the development and implementation of robust and effective knowledge translation interventions that help span the research-practice gap. PMID:25060959

The contribution of conceptual frameworks to knowledge translation interventions in physical therapy.

PubMed

Hudon, Anne; Gervais, Mathieu-Joël; Hunt, Matthew

2015-04-01

There is growing recognition of the importance of knowledge translation activities in physical therapy to ensure that research findings are integrated into clinical practice, and increasing numbers of knowledge translation interventions are being conducted. Although various frameworks have been developed to guide and facilitate the process of translating knowledge into practice, these tools have been infrequently used in physical therapy knowledge translation studies to date. Knowledge translation in physical therapy implicates multiple stakeholders and environments and involves numerous steps. In light of this complexity, the use of explicit conceptual frameworks by clinicians and researchers conducting knowledge translation interventions is associated with a range of potential benefits. This perspective article argues that such frameworks are important resources to promote the uptake of new evidence in physical therapist practice settings. Four key benefits associated with the use of conceptual frameworks in designing and implementing knowledge translation interventions are identified, and limits related to their use are considered. A sample of 5 conceptual frameworks is evaluated, and how they address common barriers to knowledge translation in physical therapy is assessed. The goal of this analysis is to provide guidance to physical therapists seeking to identify a framework to support the design and implementation of a knowledge translation intervention. Finally, the use of a conceptual framework is illustrated through a case example. Increased use of conceptual frameworks can have a positive impact on the field of knowledge translation in physical therapy and support the development and implementation of robust and effective knowledge translation interventions that help span the research-practice gap. © 2015 American Physical Therapy Association.

Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.

PubMed

Shafiee, Mohammad Javad; Chung, Audrey G; Khalvati, Farzad; Haider, Masoom A; Wong, Alexander

2017-10-01

While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers.

PubMed

Buckler, Andrew J; Liu, Tiffany Ting; Savig, Erica; Suzek, Baris E; Ouellette, M; Danagoulian, J; Wernsing, G; Rubin, Daniel L; Paik, David

2013-08-01

A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image post-processing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

RHSEG and Subdue: Background and Preliminary Approach for Combining these Technologies for Enhanced Image Data Analysis, Mining and Knowledge Discovery

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cook, Diane J.

2008-01-01

Under a project recently selected for funding by NASA's Science Mission Directorate under the Applied Information Systems Research (AISR) program, Tilton and Cook will design and implement the integration of the Subdue graph based knowledge discovery system, developed at the University of Texas Arlington and Washington State University, with image segmentation hierarchies produced by the RHSEG software, developed at NASA GSFC, and perform pilot demonstration studies of data analysis, mining and knowledge discovery on NASA data. Subdue represents a method for discovering substructures in structural databases. Subdue is devised for general-purpose automated discovery, concept learning, and hierarchical clustering, with or without domain knowledge. Subdue was developed by Cook and her colleague, Lawrence B. Holder. For Subdue to be effective in finding patterns in imagery data, the data must be abstracted up from the pixel domain. An appropriate abstraction of imagery data is a segmentation hierarchy: a set of several segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. The RHSEG program, a recursive approximation to a Hierarchical Segmentation approach (HSEG), can produce segmentation hierarchies quickly and effectively for a wide variety of images. RHSEG and HSEG were developed at NASA GSFC by Tilton. In this presentation we provide background on the RHSEG and Subdue technologies and present a preliminary analysis on how RHSEG and Subdue may be combined to enhance image data analysis, mining and knowledge discovery.

Predicting future discoveries from current scientific literature.

PubMed

Petrič, Ingrid; Cestnik, Bojan

2014-01-01

Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries. We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses. In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature.

Discovery Mechanisms for the Sensor Web

PubMed Central

Jirka, Simon; Bröring, Arne; Stasch, Christoph

2009-01-01

This paper addresses the discovery of sensors within the OGC Sensor Web Enablement framework. Whereas services like the OGC Web Map Service or Web Coverage Service are already well supported through catalogue services, the field of sensor networks and the according discovery mechanisms is still a challenge. The focus within this article will be on the use of existing OGC Sensor Web components for realizing a discovery solution. After discussing the requirements for a Sensor Web discovery mechanism, an approach will be presented that was developed within the EU funded project “OSIRIS”. This solution offers mechanisms to search for sensors, exploit basic semantic relationships, harvest sensor metadata and integrate sensor discovery into already existing catalogues. PMID:22574038

Teachers' Journal Club: Bridging between the Dynamics of Biological Discoveries and Biology Teachers

ERIC Educational Resources Information Center

Brill, Gilat; Falk, Hedda; Yarden, Anat

2003-01-01

Since biology is one of the most dynamic research fields within the natural sciences, the gap between the accumulated knowledge in biology and the knowledge that is taught in schools, increases rapidly with time. Our long-term objective is to develop means to bridge between the dynamics of biological discoveries and the biology teachers and…

A Content-Adaptive Analysis and Representation Framework for Audio Event Discovery from "Unscripted" Multimedia

NASA Astrophysics Data System (ADS)

Radhakrishnan, Regunathan; Divakaran, Ajay; Xiong, Ziyou; Otsuka, Isao

2006-12-01

We propose a content-adaptive analysis and representation framework to discover events using audio features from "unscripted" multimedia such as sports and surveillance for summarization. The proposed analysis framework performs an inlier/outlier-based temporal segmentation of the content. It is motivated by the observation that "interesting" events in unscripted multimedia occur sparsely in a background of usual or "uninteresting" events. We treat the sequence of low/mid-level features extracted from the audio as a time series and identify subsequences that are outliers. The outlier detection is based on eigenvector analysis of the affinity matrix constructed from statistical models estimated from the subsequences of the time series. We define the confidence measure on each of the detected outliers as the probability that it is an outlier. Then, we establish a relationship between the parameters of the proposed framework and the confidence measure. Furthermore, we use the confidence measure to rank the detected outliers in terms of their departures from the background process. Our experimental results with sequences of low- and mid-level audio features extracted from sports video show that "highlight" events can be extracted effectively as outliers from a background process using the proposed framework. We proceed to show the effectiveness of the proposed framework in bringing out suspicious events from surveillance videos without any a priori knowledge. We show that such temporal segmentation into background and outliers, along with the ranking based on the departure from the background, can be used to generate content summaries of any desired length. Finally, we also show that the proposed framework can be used to systematically select "key audio classes" that are indicative of events of interest in the chosen domain.

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

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

McDermott, Jason E.; Wang, Jing; Mitchell, Hugh D.

2013-01-01

The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities both for purely statistical and expert knowledge-based approaches and would benefit from improved integration of the two. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges thatmore » have been encountered. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to biomarker discovery and characterization are key to future success in the biomarker field. We will describe our recommendations of possible approaches to this problem including metrics for the evaluation of biomarkers.« less

Computational functional genomics-based approaches in analgesic drug discovery and repurposing.

PubMed

Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred; Lötsch, Jörn

2018-06-01

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.

A Location Aware Middleware Framework for Collaborative Visual Information Discovery and Retrieval

DTIC Science & Technology

2017-09-14

Information Discovery and Retrieval Andrew J.M. Compton Follow this and additional works at: https://scholar.afit.edu/etd Part of the Digital...and Dissertations by an authorized administrator of AFIT Scholar. For more information , please contact richard.mansfield@afit.edu. Recommended Citation...

Bridging the translational gap: collaborative drug development and dispelling the stigma of commercialization.

PubMed

Yu, Helen W H

2016-02-01

The current drug discovery and development process is stalling the translation of basic science into lifesaving products. Known as the 'Valley of Death', the traditional technology transfer model fails to bridge the gap between early-stage discoveries and preclinical research to advance innovations beyond the discovery phase. In addition, the stigma associated with 'commercialization' detracts from the importance of efficient translation of basic research. Here, I introduce a drug discovery model whereby the respective expertise of academia and industry are brought together to take promising discoveries through to proof of concept as a way to derisk the drug discovery and development process. Known as the 'integrated drug discovery model', I examine here the extent to which existing legal frameworks support this model. Copyright © 2015 Elsevier Ltd. All rights reserved.

From pull-down data to protein interaction networks and complexes with biological relevance.

PubMed

Zhang, Bing; Park, Byung-Hoon; Karpinets, Tatiana; Samatova, Nagiza F

2008-04-01

Recent improvements in high-throughput Mass Spectrometry (MS) technology have expedited genome-wide discovery of protein-protein interactions by providing a capability of detecting protein complexes in a physiological setting. Computational inference of protein interaction networks and protein complexes from MS data are challenging. Advances are required in developing robust and seamlessly integrated procedures for assessment of protein-protein interaction affinities, mathematical representation of protein interaction networks, discovery of protein complexes and evaluation of their biological relevance. A multi-step but easy-to-follow framework for identifying protein complexes from MS pull-down data is introduced. It assesses interaction affinity between two proteins based on similarity of their co-purification patterns derived from MS data. It constructs a protein interaction network by adopting a knowledge-guided threshold selection method. Based on the network, it identifies protein complexes and infers their core components using a graph-theoretical approach. It deploys a statistical evaluation procedure to assess biological relevance of each found complex. On Saccharomyces cerevisiae pull-down data, the framework outperformed other more complicated schemes by at least 10% in F(1)-measure and identified 610 protein complexes with high-functional homogeneity based on the enrichment in Gene Ontology (GO) annotation. Manual examination of the complexes brought forward the hypotheses on cause of false identifications. Namely, co-purification of different protein complexes as mediated by a common non-protein molecule, such as DNA, might be a source of false positives. Protein identification bias in pull-down technology, such as the hydrophilic bias could result in false negatives.

Learning in the context of distribution drift

DTIC Science & Technology

2017-05-09

published in the leading data mining journal, Data Mining and Knowledge Discovery (Webb et. al., 2016)1. We have shown that the previous qualitative...learner Low-bias learner Aggregated classifier Figure 7: Architecture for learning fr m streaming data in th co text of variable or unknown...Learning limited dependence Bayesian classifiers, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD

A Bioinformatic Approach to Inter Functional Interactions within Protein Sequences

DTIC Science & Technology

2009-02-23

AFOSR/AOARD Reference Number: USAFAOGA07: FA4869-07-1-4050 AFOSR/AOARD Program Manager : Hiroshi Motoda, Ph.D. Period of...Conference on Knowledge Discovery and Data Mining.) In a separate study we have applied our approaches to the problem of whole genome alignment. We have...SIGKDD Conference on Knowledge Discovery and Data Mining Attached. Interactions: Please list: (a) Participation/presentations at meetings

k-neighborhood Decentralization: A Comprehensive Solution to Index the UMLS for Large Scale Knowledge Discovery

PubMed Central

Xiang, Yang; Lu, Kewei; James, Stephen L.; Borlawsky, Tara B.; Huang, Kun; Payne, Philip R.O.

2011-01-01

The Unified Medical Language System (UMLS) is the largest thesaurus in the biomedical informatics domain. Previous works have shown that knowledge constructs comprised of transitively-associated UMLS concepts are effective for discovering potentially novel biomedical hypotheses. However, the extremely large size of the UMLS becomes a major challenge for these applications. To address this problem, we designed a k-neighborhood Decentralization Labeling Scheme (kDLS) for the UMLS, and the corresponding method to effectively evaluate the kDLS indexing results. kDLS provides a comprehensive solution for indexing the UMLS for very efficient large scale knowledge discovery. We demonstrated that it is highly effective to use kDLS paths to prioritize disease-gene relations across the whole genome, with extremely high fold-enrichment values. To our knowledge, this is the first indexing scheme capable of supporting efficient large scale knowledge discovery on the UMLS as a whole. Our expectation is that kDLS will become a vital engine for retrieving information and generating hypotheses from the UMLS for future medical informatics applications. PMID:22154838

k-Neighborhood decentralization: a comprehensive solution to index the UMLS for large scale knowledge discovery.

PubMed

Xiang, Yang; Lu, Kewei; James, Stephen L; Borlawsky, Tara B; Huang, Kun; Payne, Philip R O

2012-04-01

The Unified Medical Language System (UMLS) is the largest thesaurus in the biomedical informatics domain. Previous works have shown that knowledge constructs comprised of transitively-associated UMLS concepts are effective for discovering potentially novel biomedical hypotheses. However, the extremely large size of the UMLS becomes a major challenge for these applications. To address this problem, we designed a k-neighborhood Decentralization Labeling Scheme (kDLS) for the UMLS, and the corresponding method to effectively evaluate the kDLS indexing results. kDLS provides a comprehensive solution for indexing the UMLS for very efficient large scale knowledge discovery. We demonstrated that it is highly effective to use kDLS paths to prioritize disease-gene relations across the whole genome, with extremely high fold-enrichment values. To our knowledge, this is the first indexing scheme capable of supporting efficient large scale knowledge discovery on the UMLS as a whole. Our expectation is that kDLS will become a vital engine for retrieving information and generating hypotheses from the UMLS for future medical informatics applications. Copyright © 2011 Elsevier Inc. All rights reserved.

Concept of Operations for Collaboration and Discovery from Big Data Across Enterprise Data Warehouses

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

Olama, Mohammed M; Nutaro, James J; Sukumar, Sreenivas R

2013-01-01

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Optionsmore » that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.« less

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for internal climate studies in data mining and knowledge discovery techniques are used. However, it is necessary to use the KDD Approach and DM techniques in the climatic studies, specific interpreter of climate modeling result.

Essential Annotation Schema for Ecology (EASE)—A framework supporting the efficient data annotation and faceted navigation in ecology

PubMed Central

Eichenberg, David; Liebergesell, Mario; König-Ries, Birgitta; Wirth, Christian

2017-01-01

Ecology has become a data intensive science over the last decades which often relies on the reuse of data in cross-experimental analyses. However, finding data which qualifies for the reuse in a specific context can be challenging. It requires good quality metadata and annotations as well as efficient search strategies. To date, full text search (often on the metadata only) is the most widely used search strategy although it is known to be inaccurate. Faceted navigation is providing a filter mechanism which is based on fine granular metadata, categorizing search objects along numeric and categorical parameters relevant for their discovery. Selecting from these parameters during a full text search creates a system of filters which allows to refine and improve the results towards more relevance. We developed a framework for the efficient annotation and faceted navigation in ecology. It consists of an XML schema for storing the annotation of search objects and is accompanied by a vocabulary focused on ecology to support the annotation process. The framework consolidates ideas which originate from widely accepted metadata standards, textbooks, scientific literature, and vocabularies as well as from expert knowledge contributed by researchers from ecology and adjacent disciplines. PMID:29023519

A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes

PubMed Central

Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

2013-01-01

In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users” actions to gain knowledge about their habits and preferences. PMID:23435057

Mississippi Curriculum Framework for Technology Discovery (9th Grade). CIP: 00.0253.

ERIC Educational Resources Information Center

Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

This document, which is intended for technology educators in Mississippi, outlines a technology discovery course in which a modular instruction approach allows ninth graders to experience various workplace technologies within four career cluster areas: agriculture/natural resources technology, business/marketing technology, health/human services…

Intelligent resource discovery using ontology-based resource profiles

NASA Technical Reports Server (NTRS)

Hughes, J. Steven; Crichton, Dan; Kelly, Sean; Crichton, Jerry; Tran, Thuy

2004-01-01

Successful resource discovery across heterogeneous repositories is strongly dependent on the semantic and syntactic homogeneity of the associated resource descriptions. Ideally, resource descriptions are easily extracted from pre-existing standardized sources, expressed using standard syntactic and semantic structures, and managed and accessed within a distributed, flexible, and scaleable software framework.

The Noble Gases in A-Level Chemistry.

ERIC Educational Resources Information Center

Marchant, G. W.

1983-01-01

Suggests two methods of developing the study of the noble gases: first, the discovery of the elements and recent discovery of xenon show the human face of chemistry (historical development); second, the properties of noble gas compounds (particularly xenon) can be used to test the framework of conventional chemistry. (Author/JM)

Ecologies, outreach, and the evolution of medical libraries.

PubMed

Shen, Bern

2005-10-01

What are some of the forces shaping the evolution of medical libraries, and where might they lead? Published literature in the fields of library and information sciences, technology, health services research, and business was consulted. Medical libraries currently have a modest footprint in most consumers' personal health ecologies, the network of resources and activities they use to improve their health. They also occupy a relatively small space in the health care, information, and business ecologies of which they are a part. Several trends in knowledge discovery, technology, and social organizations point to ways in which the roles of medical libraries might grow and become more complex. As medical libraries evolve and reach out to previously underserved communities, an ecological approach can serve as a useful organizing framework for the forces shaping this evolution.

Extracting nursing practice patterns from structured labor and delivery data sets.

PubMed

Hall, Eric S; Thornton, Sidney N

2007-10-11

This study was designed to demonstrate the feasibility of a computerized care process model that provides real-time case profiling and outcome forecasting. A methodology was defined for extracting nursing practice patterns from structured point-of-care data collected using the labor and delivery information system at Intermountain Healthcare. Data collected during January 2006 were retrieved from Intermountain Healthcare's enterprise data warehouse for use in the study. The knowledge discovery in databases process provided a framework for data analysis including data selection, preprocessing, data-mining, and evaluation. Development of an interactive data-mining tool and construction of a data model for stratification of patient records into profiles supported the goals of the study. Five benefits of the practice pattern extraction capability, which extend to other clinical domains, are listed with supporting examples.

Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine.

PubMed

Ping, Peipei; Hermjakob, Henning; Polson, Jennifer S; Benos, Panagiotis V; Wang, Wei

2018-04-27

In the digital age of cardiovascular medicine, the rate of biomedical discovery can be greatly accelerated by the guidance and resources required to unearth potential collections of knowledge. A unified computational platform leverages metadata to not only provide direction but also empower researchers to mine a wealth of biomedical information and forge novel mechanistic insights. This review takes the opportunity to present an overview of the cloud-based computational environment, including the functional roles of metadata, the architecture schema of indexing and search, and the practical scenarios of machine learning-supported molecular signature extraction. By introducing several established resources and state-of-the-art workflows, we share with our readers a broadly defined informatics framework to phenotype cardiovascular health and disease. © 2018 American Heart Association, Inc.

A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells

PubMed Central

Karlsson, Alexander; Riveiro, Maria; Améen, Caroline; Åkesson, Karolina; Andersson, Christian X.; Sartipy, Peter; Synnergren, Jane

2017-01-01

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic- and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics. PMID:28654683

A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells.

PubMed

Ulfenborg, Benjamin; Karlsson, Alexander; Riveiro, Maria; Améen, Caroline; Åkesson, Karolina; Andersson, Christian X; Sartipy, Peter; Synnergren, Jane

2017-01-01

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic- and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

Discovery and problem solving: Triangulation as a weak heuristic

NASA Technical Reports Server (NTRS)

Rochowiak, Daniel

1987-01-01

Recently the artificial intelligence community has turned its attention to the process of discovery and found that the history of science is a fertile source for what Darden has called compiled hindsight. Such hindsight generates weak heuristics for discovery that do not guarantee that discoveries will be made but do have proven worth in leading to discoveries. Triangulation is one such heuristic that is grounded in historical hindsight. This heuristic is explored within the general framework of the BACON, GLAUBER, STAHL, DALTON, and SUTTON programs. In triangulation different bases of information are compared in an effort to identify gaps between the bases. Thus, assuming that the bases of information are relevantly related, the gaps that are identified should be good locations for discovery and robust analysis.

Theoretical Framework of Researcher Knowledge Development in Mathematics Education

ERIC Educational Resources Information Center

Kontorovich, Igor'

2016-01-01

The goal of this paper is to present a framework of researcher knowledge development in conducting a study in mathematics education. The key components of the framework are: knowledge germane to conducting a particular study, processes of knowledge accumulation, and catalyzing filters that influence a researcher's decision making. The components…

Knowledge Retrieval Solutions.

ERIC Educational Resources Information Center

Khan, Kamran

1998-01-01

Excalibur RetrievalWare offers true knowledge retrieval solutions. Its fundamental technologies, Adaptive Pattern Recognition Processing and Semantic Networks, have capabilities for knowledge discovery and knowledge management of full-text, structured and visual information. The software delivers a combination of accuracy, extensibility,…

Knowledge extraction from evolving spiking neural networks with rank order population coding.

PubMed

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Optical methods in nano-biotechnology

NASA Astrophysics Data System (ADS)

Bruno, Luigi; Gentile, Francesco

2016-01-01

A scientific theory is not a mathematical paradigm. It is a framework that explains natural facts and may predict future observations. A scientific theory may be modified, improved, or rejected. Science is less a collection of theories and more the process that brings either to deny some hypothesis, maintain or accept somehow universal beliefs (or disbeliefs), and create new models that may improve or replace precedent theories. This process cannot be entrusted to common sense, personal experiences or anecdotes (many precepts in physics are indeed counterintuitive), but on a rigorous design, observation and rational to statistical analysis of new experiments. Scientific results are always provisional: scientists rarely proclaim an absolute truth or absolute certainty. Uncertainty is inevitable at the frontiers of knowledge. Notably, this is the definition of the scientific method and what we have written in the above echoes the opinion Marcia McNutt who is the Editor of Science 'Science is a method for deciding whether what we choose to believe has a basis in the laws of nature or not'. A new discovery, a new theory that explains that discovery and the scientific method itself need observations, verifications and are susceptible of falsification.

A Projection and Density Estimation Method for Knowledge Discovery

PubMed Central

Stanski, Adam; Hellwich, Olaf

2012-01-01

A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features. PMID:23049675

A semantic web ontology for small molecules and their biological targets.

PubMed

Choi, Jooyoung; Davis, Melissa J; Newman, Andrew F; Ragan, Mark A

2010-05-24

A wide range of data on sequences, structures, pathways, and networks of genes and gene products is available for hypothesis testing and discovery in biological and biomedical research. However, data describing the physical, chemical, and biological properties of small molecules have not been well-integrated with these resources. Semantically rich representations of chemical data, combined with Semantic Web technologies, have the potential to enable the integration of small molecule and biomolecular data resources, expanding the scope and power of biomedical and pharmacological research. We employed the Semantic Web technologies Resource Description Framework (RDF) and Web Ontology Language (OWL) to generate a Small Molecule Ontology (SMO) that represents concepts and provides unique identifiers for biologically relevant properties of small molecules and their interactions with biomolecules, such as proteins. We instanced SMO using data from three public data sources, i.e., DrugBank, PubChem and UniProt, and converted to RDF triples. Evaluation of SMO by use of predetermined competency questions implemented as SPARQL queries demonstrated that data from chemical and biomolecular data sources were effectively represented and that useful knowledge can be extracted. These results illustrate the potential of Semantic Web technologies in chemical, biological, and pharmacological research and in drug discovery.

OSSOS: X. How to use a Survey Simulator: Statistical Testing of Dynamical Models Against the Real Kuiper Belt

NASA Astrophysics Data System (ADS)

Lawler, Samantha M.; Kavelaars, J. J.; Alexandersen, Mike; Bannister, Michele T.; Gladman, Brett; Petit, Jean-Marc; Shankman, Cory

2018-05-01

All surveys include observational biases, which makes it impossible to directly compare properties of discovered trans-Neptunian Objects (TNOs) with dynamical models. However, by carefully keeping track of survey pointings on the sky, detection limits, tracking fractions, and rate cuts, the biases from a survey can be modelled in Survey Simulator software. A Survey Simulator takes an intrinsic orbital model (from, for example, the output of a dynamical Kuiper belt emplacement simulation) and applies the survey biases, so that the biased simulated objects can be directly compared with real discoveries. This methodology has been used with great success in the Outer Solar System Origins Survey (OSSOS) and its predecessor surveys. In this chapter, we give four examples of ways to use the OSSOS Survey Simulator to gain knowledge about the true structure of the Kuiper Belt. We demonstrate how to statistically compare different dynamical model outputs with real TNO discoveries, how to quantify detection biases within a TNO population, how to measure intrinsic population sizes, and how to use upper limits from non-detections. We hope this will provide a framework for dynamical modellers to statistically test the validity of their models.

Drug target ontology to classify and integrate drug discovery data.

PubMed

Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C

2017-11-09

One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Informing child welfare policy and practice: using knowledge discovery and data mining technology via a dynamic Web site.

PubMed

Duncan, Dean F; Kum, Hye-Chung; Weigensberg, Elizabeth Caplick; Flair, Kimberly A; Stewart, C Joy

2008-11-01

Proper management and implementation of an effective child welfare agency requires the constant use of information about the experiences and outcomes of children involved in the system, emphasizing the need for comprehensive, timely, and accurate data. In the past 20 years, there have been many advances in technology that can maximize the potential of administrative data to promote better evaluation and management in the field of child welfare. Specifically, this article discusses the use of knowledge discovery and data mining (KDD), which makes it possible to create longitudinal data files from administrative data sources, extract valuable knowledge, and make the information available via a user-friendly public Web site. This article demonstrates a successful project in North Carolina where knowledge discovery and data mining technology was used to develop a comprehensive set of child welfare outcomes available through a public Web site to facilitate information sharing of child welfare data to improve policy and practice.

Preparing learners with partly incorrect intuitive prior knowledge for learning

PubMed Central

Ohst, Andrea; Fondu, Béatrice M. E.; Glogger, Inga; Nückles, Matthias; Renkl, Alexander

2014-01-01

Learners sometimes have incoherent and fragmented intuitive prior knowledge that is (partly) “incompatible” with the to-be-learned contents. Such knowledge in pieces can cause conceptual disorientation and cognitive overload while learning. We hypothesized that a pre-training intervention providing a generalized schema as a structuring framework for such knowledge in pieces would support (re)organizing-processes of prior knowledge and thus reduce unnecessary cognitive load during subsequent learning. Fifty-six student teachers participated in the experiment. A framework group underwent a pre-training intervention providing a generalized, categorical schema for categorizing primary learning strategies and related but different strategies as a cognitive framework for (re-)organizing their prior knowledge. Our control group received comparable factual information but no framework. Afterwards, all participants learned about primary learning strategies. The framework group claimed to possess higher levels of interest and self-efficacy, achieved higher learning outcomes, and learned more efficiently. Hence, providing a categorical framework can help overcome the barrier of incorrect prior knowledge in pieces. PMID:25071638

Developing integrated crop knowledge networks to advance candidate gene discovery.

PubMed

Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

2016-12-01

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology

PubMed Central

Zhang, Guang Lan; Riemer, Angelika B.; Keskin, Derin B.; Chitkushev, Lou; Reinherz, Ellis L.; Brusic, Vladimir

2014-01-01

High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/ PMID:24705205

HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology.

PubMed

Zhang, Guang Lan; Riemer, Angelika B; Keskin, Derin B; Chitkushev, Lou; Reinherz, Ellis L; Brusic, Vladimir

2014-01-01

High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/.

a Conceptual Framework for Virtual Geographic Environments Knowledge Engineering

NASA Astrophysics Data System (ADS)

You, Lan; Lin, Hui

2016-06-01

VGE geographic knowledge refers to the abstract and repeatable geo-information which is related to the geo-science problem, geographical phenomena and geographical laws supported by VGE. That includes expert experiences, evolution rule, simulation processes and prediction results in VGE. This paper proposes a conceptual framework for VGE knowledge engineering in order to effectively manage and use geographic knowledge in VGE. Our approach relies on previous well established theories on knowledge engineering and VGE. The main contribution of this report is following: (1) The concepts of VGE knowledge and VGE knowledge engineering which are defined clearly; (2) features about VGE knowledge different with common knowledge; (3) geographic knowledge evolution process that help users rapidly acquire knowledge in VGE; and (4) a conceptual framework for VGE knowledge engineering providing the supporting methodologies system for building an intelligent VGE. This conceptual framework systematically describes the related VGE knowledge theories and key technologies. That will promote the rapid transformation from geodata to geographic knowledge, and furtherly reduce the gap between the data explosion and knowledge absence.

Developing a framework for transferring knowledge into action: a thematic analysis of the literature

PubMed Central

Ward, Vicky; House, Allan; Hamer, Susan

2010-01-01

Objectives Although there is widespread agreement about the importance of transferring knowledge into action, we still lack high quality information about what works, in which settings and with whom. Whilst there are a large number of models and theories for knowledge transfer interventions, they are untested meaning that their applicability and relevance is largely unknown. This paper describes the development of a conceptual framework of translating knowledge into action and discusses how it can be used for developing a useful model of the knowledge transfer process. Methods A narrative review of the knowledge transfer literature identified 28 different models which explained all or part of the knowledge transfer process. The models were subjected to a thematic analysis to identify individual components and the types of processes used when transferring knowledge into action. The results were used to build a conceptual framework of the process. Results Five common components of the knowledge transfer process were identified: problem identification and communication; knowledge/research development and selection; analysis of context; knowledge transfer activities or interventions; and knowledge/research utilization. We also identified three types of knowledge transfer processes: a linear process; a cyclical process; and a dynamic multidirectional process. From these results a conceptual framework of knowledge transfer was developed. The framework illustrates the five common components of the knowledge transfer process and shows that they are connected via a complex, multidirectional set of interactions. As such the framework allows for the individual components to occur simultaneously or in any given order and to occur more than once during the knowledge transfer process. Conclusion Our framework provides a foundation for gathering evidence from case studies of knowledge transfer interventions. We propose that future empirical work is designed to test and refine the relevant importance and applicability of each of the components in order to build more useful models of knowledge transfer which can serve as a practical checklist for planning or evaluating knowledge transfer activities. PMID:19541874

The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space.

PubMed

Nicolaou, Christos A; Watson, Ian A; Hu, Hong; Wang, Jibo

2016-07-25

Venturing into the immensity of the small molecule universe to identify novel chemical structure is a much discussed objective of many methods proposed by the chemoinformatics community. To this end, numerous approaches using techniques from the fields of computational de novo design, virtual screening and reaction informatics, among others, have been proposed. Although in principle this objective is commendable, in practice there are several obstacles to useful exploitation of the chemical space. Prime among them are the sheer number of theoretically feasible compounds and the practical concern regarding the synthesizability of the chemical structures conceived using in silico methods. We present the Proximal Lilly Collection initiative implemented at Eli Lilly and Co. with the aims to (i) define the chemical space of small, drug-like compounds that could be synthesized using in-house resources and (ii) facilitate access to compounds in this large space for the purposes of ongoing drug discovery efforts. The implementation of PLC relies on coupling access to available synthetic knowledge and resources with chemo/reaction informatics techniques and tools developed for this purpose. We describe in detail the computational framework supporting this initiative and elaborate on the characteristics of the PLC virtual collection of compounds. As an example of the opportunities provided to drug discovery researchers by easy access to a large, realistically feasible virtual collection such as the PLC, we describe a recent application of the technology that led to the discovery of selective kinase inhibitors.

Biomimetic mineralization of metal-organic frameworks around polysaccharides.

PubMed

Liang, Kang; Wang, Ru; Boutter, Manon; Doherty, Cara M; Mulet, Xavier; Richardson, Joseph J

2017-01-19

Biomimetic mineralization exploits natural biomineralization processes for the design and fabrication of synthetic functional materials. Here, we report for the first time the use of carbohydrates (polysaccharides) for the biomimetic crystallization of metal-organic frameworks. This discovery greatly expands the potential and diversity of biomimetic approaches for the design, synthesis, and functionalization of new bio-metal-organic framework composite materials.

Frontiers for research on the ecology of plant-pathogenic bacteria: fundamentals for sustainability: Challenges in Bacterial Molecular Plant Pathology.

PubMed

Morris, Cindy E; Barny, Marie-Anne; Berge, Odile; Kinkel, Linda L; Lacroix, Christelle

2017-02-01

Methods to ensure the health of crops owe their efficacy to the extent to which we understand the ecology and biology of environmental microorganisms and the conditions under which their interactions with plants lead to losses in crop quality or yield. However, in the pursuit of this knowledge, notions of the ecology of plant-pathogenic microorganisms have been reduced to a plant-centric and agro-centric focus. With increasing global change, i.e. changes that encompass not only climate, but also biodiversity, the geographical distribution of biomes, human demographic and socio-economic adaptations and land use, new plant health problems will emerge via a range of processes influenced by these changes. Hence, knowledge of the ecology of plant pathogens will play an increasingly important role in the anticipation and response to disease emergence. Here, we present our opinion on the major challenges facing the study of the ecology of plant-pathogenic bacteria. We argue that the discovery of markedly novel insights into the ecology of plant-pathogenic bacteria is most likely to happen within a framework of more extensive scales of space, time and biotic interactions than those that currently guide much of the research on these bacteria. This will set a context that is more propitious for the discovery of unsuspected drivers of the survival and diversification of plant-pathogenic bacteria and of the factors most critical for disease emergence, and will set the foundation for new approaches to the sustainable management of plant health. We describe the contextual background of, justification for and specific research questions with regard to the following challenges: Development of terminology to describe plant-bacterial relationships in terms of bacterial fitness. Definition of the full scope of the environments in which plant-pathogenic bacteria reside or survive. Delineation of pertinent phylogenetic contours of plant-pathogenic bacteria and naming of strains independent of their presumed life style. Assessment of how traits of plant-pathogenic bacteria evolve within the overall framework of their life history. Exploration of possible beneficial ecosystem services contributed to by plant-pathogenic bacteria. © 2016 BSPP AND JOHN WILEY & SONS LTD.

On the Growth of Scientific Knowledge: Yeast Biology as a Case Study

PubMed Central

He, Xionglei; Zhang, Jianzhi

2009-01-01

The tempo and mode of human knowledge expansion is an enduring yet poorly understood topic. Through a temporal network analysis of three decades of discoveries of protein interactions and genetic interactions in baker's yeast, we show that the growth of scientific knowledge is exponential over time and that important subjects tend to be studied earlier. However, expansions of different domains of knowledge are highly heterogeneous and episodic such that the temporal turnover of knowledge hubs is much greater than expected by chance. Familiar subjects are preferentially studied over new subjects, leading to a reduced pace of innovation. While research is increasingly done in teams, the number of discoveries per researcher is greater in smaller teams. These findings reveal collective human behaviors in scientific research and help design better strategies in future knowledge exploration. PMID:19300476

On the growth of scientific knowledge: yeast biology as a case study.

PubMed

He, Xionglei; Zhang, Jianzhi

2009-03-01

The tempo and mode of human knowledge expansion is an enduring yet poorly understood topic. Through a temporal network analysis of three decades of discoveries of protein interactions and genetic interactions in baker's yeast, we show that the growth of scientific knowledge is exponential over time and that important subjects tend to be studied earlier. However, expansions of different domains of knowledge are highly heterogeneous and episodic such that the temporal turnover of knowledge hubs is much greater than expected by chance. Familiar subjects are preferentially studied over new subjects, leading to a reduced pace of innovation. While research is increasingly done in teams, the number of discoveries per researcher is greater in smaller teams. These findings reveal collective human behaviors in scientific research and help design better strategies in future knowledge exploration.

Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation.

PubMed

Tan, Yuxiang; Hu, Yong; Liu, Xiaoxiao; Yin, Zhinan; Chen, Xue-Wen; Liu, Mei

2016-11-01

Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions. Copyright © 2016 Elsevier Inc. All rights reserved.

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

PubMed Central

Lötsch, Jörn; Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred

2017-01-01

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence. PMID:28848388

Integrative Convergence in Neuroscience: Trajectories, Problems, and the Need for a Progressive Neurobioethics

NASA Astrophysics Data System (ADS)

Giordano, J.

The advanced integrative scientific convergence (AISC) model represents a viable approach to neuroscience. Beyond simple multi-disciplinarity, the AISC model unifies constituent scientific and technological fields to foster innovation, invention and new ways of addressing seemingly intractable questions. In this way, AISC can yield novel methods and foster new trajectories of knowledge and discovery, and yield new epistemologies. As stand-alone disciplines, each and all of the constituent fields generate practical and ethical issues, and their convergence may establish a unique set of both potential benefits and problems. To effectively attend to these contingencies requires pragmatic assessment of the actual capabilities and limits of neurofocal AISC, and an openness to what new knowledge and scientific/technological achievements may be produced, and how such outcomes can affect humanity, the human condition, society and the global environment. It is proposed that a progressive neurobioethics may be needed to establish both a meta-ethical framework upon which to structure ethical decisions, and a system and method of ethics that is inclusive, convergent and innovative, and in thus aligned with and meaningful to use of an AISC model in neuroscience.

Four (Algorithms) in One (Bag): An Integrative Framework of Knowledge for Teaching the Standard Algorithms of the Basic Arithmetic Operations

ERIC Educational Resources Information Center

Raveh, Ira; Koichu, Boris; Peled, Irit; Zaslavsky, Orit

2016-01-01

In this article we present an integrative framework of knowledge for teaching the standard algorithms of the four basic arithmetic operations. The framework is based on a mathematical analysis of the algorithms, a connectionist perspective on teaching mathematics and an analogy with previous frameworks of knowledge for teaching arithmetic…

Applying Knowledge Discovery in Databases in Public Health Data Set: Challenges and Concerns

PubMed Central

Volrathongchia, Kanittha

2003-01-01

In attempting to apply Knowledge Discovery in Databases (KDD) to generate a predictive model from a health care dataset that is currently available to the public, the first step is to pre-process the data to overcome the challenges of missing data, redundant observations, and records containing inaccurate data. This study will demonstrate how to use simple pre-processing methods to improve the quality of input data. PMID:14728545

Big, Deep, and Smart Data in Scanning Probe Microscopy

DOE PAGES

Kalinin, Sergei V.; Strelcov, Evgheni; Belianinov, Alex; ...

2016-09-27

Scanning probe microscopy techniques open the door to nanoscience and nanotechnology by enabling imaging and manipulation of structure and functionality of matter on nanometer and atomic scales. We analyze the discovery process by SPM in terms of information flow from tip-surface junction to the knowledge adoption by scientific community. Furthermore, we discuss the challenges and opportunities offered by merging of SPM and advanced data mining, visual analytics, and knowledge discovery technologies.

An Alternative Time for Telling: When Conceptual Instruction Prior to Exploration Improves Mathematical Knowledge

ERIC Educational Resources Information Center

Fyfe, Emily R.; DeCaro, Marci S.; Rittle-Johnson, Bethany

2013-01-01

An emerging consensus suggests that guided discovery, which combines discovery and instruction, is a more effective educational approach than either one in isolation. The goal of this study was to examine two specific forms of guided discovery, testing whether conceptual instruction should precede or follow exploratory problem solving. In both…

Contributing, Exchanging and Linking for Learning: Supporting Web Co-Discovery in One-to-One Environments

ERIC Educational Resources Information Center

Liu, Chen-Chung; Don, Ping-Hsing; Chung, Chen-Wei; Lin, Shao-Jun; Chen, Gwo-Dong; Liu, Baw-Jhiune

2010-01-01

While Web discovery is usually undertaken as a solitary activity, Web co-discovery may transform Web learning activities from the isolated individual search process into interactive and collaborative knowledge exploration. Recent studies have proposed Web co-search environments on a single computer, supported by multiple one-to-one technologies.…

Knowledge Management in Higher Education: A Knowledge Repository Approach

ERIC Educational Resources Information Center

Wedman, John; Wang, Feng-Kwei

2005-01-01

One might expect higher education, where the discovery and dissemination of new and useful knowledge is vital, to be among the first to implement knowledge management practices. Surprisingly, higher education has been slow to implement knowledge management practices (Townley, 2003). This article describes an ongoing research and development effort…

Crowdsourcing Knowledge Discovery and Innovations in Medicine

PubMed Central

2014-01-01

Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. “Health hackathons” and “data marathons”, in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled. PMID:25239002

Crowdsourcing knowledge discovery and innovations in medicine.

PubMed

Celi, Leo Anthony; Ippolito, Andrea; Montgomery, Robert A; Moses, Christopher; Stone, David J

2014-09-19

Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. "Health hackathons" and "data marathons", in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled.

Empirical study using network of semantically related associations in bridging the knowledge gap.

PubMed

Abedi, Vida; Yeasin, Mohammed; Zand, Ramin

2014-11-27

The data overload has created a new set of challenges in finding meaningful and relevant information with minimal cognitive effort. However designing robust and scalable knowledge discovery systems remains a challenge. Recent innovations in the (biological) literature mining tools have opened new avenues to understand the confluence of various diseases, genes, risk factors as well as biological processes in bridging the gaps between the massive amounts of scientific data and harvesting useful knowledge. In this paper, we highlight some of the findings using a text analytics tool, called ARIANA--Adaptive Robust and Integrative Analysis for finding Novel Associations. Empirical study using ARIANA reveals knowledge discovery instances that illustrate the efficacy of such tool. For example, ARIANA can capture the connection between the drug hexamethonium and pulmonary inflammation and fibrosis that caused the tragic death of a healthy volunteer in a 2001 John Hopkins asthma study, even though the abstract of the study was not part of the semantic model. An integrated system, such as ARIANA, could assist the human expert in exploratory literature search by bringing forward hidden associations, promoting data reuse and knowledge discovery as well as stimulating interdisciplinary projects by connecting information across the disciplines.

Virtual Observatories, Data Mining, and Astroinformatics

NASA Astrophysics Data System (ADS)

Borne, Kirk

The historical, current, and future trends in knowledge discovery from data in astronomy are presented here. The story begins with a brief history of data gathering and data organization. A description of the development ofnew information science technologies for astronomical discovery is then presented. Among these are e-Science and the virtual observatory, with its data discovery, access, display, and integration protocols; astroinformatics and data mining for exploratory data analysis, information extraction, and knowledge discovery from distributed data collections; new sky surveys' databases, including rich multivariate observational parameter sets for large numbers of objects; and the emerging discipline of data-oriented astronomical research, called astroinformatics. Astroinformatics is described as the fourth paradigm of astronomical research, following the three traditional research methodologies: observation, theory, and computation/modeling. Astroinformatics research areas include machine learning, data mining, visualization, statistics, semantic science, and scientific data management.Each of these areas is now an active research discipline, with significantscience-enabling applications in astronomy. Research challenges and sample research scenarios are presented in these areas, in addition to sample algorithms for data-oriented research. These information science technologies enable scientific knowledge discovery from the increasingly large and complex data collections in astronomy. The education and training of the modern astronomy student must consequently include skill development in these areas, whose practitioners have traditionally been limited to applied mathematicians, computer scientists, and statisticians. Modern astronomical researchers must cross these traditional discipline boundaries, thereby borrowing the best of breed methodologies from multiple disciplines. In the era of large sky surveys and numerous large telescopes, the potential for astronomical discovery is equally large, and so the data-oriented research methods, algorithms, and techniques that are presented here will enable the greatest discovery potential from the ever-growing data and information resources in astronomy.

Jack Stenner: The Lexile King.

ERIC Educational Resources Information Center

Webster, Linda J.

2000-01-01

Traces the career of Jack Stenner. Stenner made the empirical discovery that observable readability could be entirely predicted from word familiarity and sentence length, and applied this "Lexile Framework"(R) to books and readers. Discusses the use of the Lexile Framework as a way to target specific readers. (SLD)

Text-based discovery in biomedicine: the architecture of the DAD-system.

PubMed

Weeber, M; Klein, H; Aronson, A R; Mork, J G; de Jong-van den Berg, L T; Vos, R

2000-01-01

Current scientific research takes place in highly specialized contexts with poor communication between disciplines as a likely consequence. Knowledge from one discipline may be useful for the other without researchers knowing it. As scientific publications are a condensation of this knowledge, literature-based discovery tools may help the individual scientist to explore new useful domains. We report on the development of the DAD-system, a concept-based Natural Language Processing system for PubMed citations that provides the biomedical researcher such a tool. We describe the general architecture and illustrate its operation by a simulation of a well-known text-based discovery: The favorable effects of fish oil on patients suffering from Raynaud's disease [1].

Which are the greatest recent discoveries and the greatest future challenges in nutrition?

PubMed

Katan, M B; Boekschoten, M V; Connor, W E; Mensink, R P; Seidell, J; Vessby, B; Willett, W

2009-01-01

Nutrition science aims to create new knowledge, but scientists rarely sit back to reflect on what nutrition research has achieved in recent decades. We report the outcome of a 1-day symposium at which the audience was asked to vote on the greatest discoveries in nutrition since 1976 and on the greatest challenges for the coming 30 years. Most of the 128 participants were Dutch scientists working in nutrition or related biomedical and public health fields. Candidate discoveries and challenges were nominated by five invited speakers and by members of the audience. Ballot forms were then prepared on which participants selected one discovery and one challenge. A total of 15 discoveries and 14 challenges were nominated. The audience elected Folic acid prevents birth defects as the greatest discovery in nutrition science since 1976. Controlling obesity and insulin resistance through activity and diet was elected as the greatest challenge for the coming 30 years. This selection was probably biased by the interests and knowledge of the speakers and the audience. For the present review, we therefore added 12 discoveries from the period 1976 to 2006 that we judged worthy of consideration, but that had not been nominated at the meeting. The meeting did not represent an objective selection process, but it did demonstrate that the past 30 years have yielded major new discoveries in nutrition and health.

Translating three states of knowledge--discovery, invention, and innovation

PubMed Central

2010-01-01

Background Knowledge Translation (KT) has historically focused on the proper use of knowledge in healthcare delivery. A knowledge base has been created through empirical research and resides in scholarly literature. Some knowledge is amenable to direct application by stakeholders who are engaged during or after the research process, as shown by the Knowledge to Action (KTA) model. Other knowledge requires multiple transformations before achieving utility for end users. For example, conceptual knowledge generated through science or engineering may become embodied as a technology-based invention through development methods. The invention may then be integrated within an innovative device or service through production methods. To what extent is KT relevant to these transformations? How might the KTA model accommodate these additional development and production activities while preserving the KT concepts? Discussion Stakeholders adopt and use knowledge that has perceived utility, such as a solution to a problem. Achieving a technology-based solution involves three methods that generate knowledge in three states, analogous to the three classic states of matter. Research activity generates discoveries that are intangible and highly malleable like a gas; development activity transforms discoveries into inventions that are moderately tangible yet still malleable like a liquid; and production activity transforms inventions into innovations that are tangible and immutable like a solid. The paper demonstrates how the KTA model can accommodate all three types of activity and address all three states of knowledge. Linking the three activities in one model also illustrates the importance of engaging the relevant stakeholders prior to initiating any knowledge-related activities. Summary Science and engineering focused on technology-based devices or services change the state of knowledge through three successive activities. Achieving knowledge implementation requires methods that accommodate these three activities and knowledge states. Accomplishing beneficial societal impacts from technology-based knowledge involves the successful progression through all three activities, and the effective communication of each successive knowledge state to the relevant stakeholders. The KTA model appears suitable for structuring and linking these processes. PMID:20205873

Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources.

PubMed

Huang, Liang-Chin; Ross, Karen E; Baffi, Timothy R; Drabkin, Harold; Kochut, Krzysztof J; Ruan, Zheng; D'Eustachio, Peter; McSkimming, Daniel; Arighi, Cecilia; Chen, Chuming; Natale, Darren A; Smith, Cynthia; Gaudet, Pascale; Newton, Alexandra C; Wu, Cathy; Kannan, Natarajan

2018-04-25

Many bioinformatics resources with unique perspectives on the protein landscape are currently available. However, generating new knowledge from these resources requires interoperable workflows that support cross-resource queries. In this study, we employ federated queries linking information from the Protein Kinase Ontology, iPTMnet, Protein Ontology, neXtProt, and the Mouse Genome Informatics to identify key knowledge gaps in the functional coverage of the human kinome and prioritize understudied kinases, cancer variants and post-translational modifications (PTMs) for functional studies. We identify 32 functional domains enriched in cancer variants and PTMs and generate mechanistic hypotheses on overlapping variant and PTM sites by aggregating information at the residue, protein, pathway and species level from these resources. We experimentally test the hypothesis that S768 phosphorylation in the C-helix of EGFR is inhibitory by showing that oncogenic variants altering S768 phosphorylation increase basal EGFR activity. In contrast, oncogenic variants altering conserved phosphorylation sites in the 'hydrophobic motif' of PKCβII (S660F and S660C) are loss-of-function in that they reduce kinase activity and enhance membrane translocation. Our studies provide a framework for integrative, consistent, and reproducible annotation of the cancer kinomes.

Interfaith education: An Islamic perspective

NASA Astrophysics Data System (ADS)

Pallavicini, Yahya Sergio Yahe

2016-08-01

According to a teaching of the Prophet Muhammad, "the quest for knowledge is the duty of each Muslim, male or female", where knowledge is meant as the discovery of the real value of things and of oneself in relationship with the world in which God has placed us. This universal dimension of knowledge is in fact a wealth of wisdom of the traditional doctrine naturally linked to the cultural and spiritual heritage of every human being and every believer of every faith. It allows for the respect of internal and external differences as positive elements of the cultural and spiritual heritage of mankind. In this sense, intercultural and interfaith education plays a fundamental role and fits naturally within the Islamic religious education framework. The author of this article is Vice-President and Imam of the Islamic Religious Community in Italy (Comunità Religiosa Islamica [COREIS] Italiana), an organisation which has been providing teachers and students with training on Islam and interfaith dialogue for almost twenty years with the support of the regional and national offices of the Italian Ministry of Public Education. Referring to existing interreligious and intercultural societies such as Azerbaijan, and a number of successful initiatives and projects, several of which COREIS is involved in, he demonstrates how interfaith education can effectively contribute to preventing the diffusion of anti-Semitism, Islamophobia and radicalism.

Knowledge engineering for adverse drug event prevention: on the design and development of a uniform, contextualized and sustainable knowledge-based framework.

PubMed

Koutkias, Vassilis; Kilintzis, Vassilis; Stalidis, George; Lazou, Katerina; Niès, Julie; Durand-Texte, Ludovic; McNair, Peter; Beuscart, Régis; Maglaveras, Nicos

2012-06-01

The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards implementation, performance and validation aspects that highlight its applicability and virtue in medication safety. Copyright © 2012 Elsevier Inc. All rights reserved.

Wains: a pattern-seeking artificial life species.

PubMed

de Buitléir, Amy; Russell, Michael; Daly, Mark

2012-01-01

We describe the initial phase of a research project to develop an artificial life framework designed to extract knowledge from large data sets with minimal preparation or ramp-up time. In this phase, we evolved an artificial life population with a new brain architecture. The agents have sufficient intelligence to discover patterns in data and to make survival decisions based on those patterns. The species uses diploid reproduction, Hebbian learning, and Kohonen self-organizing maps, in combination with novel techniques such as using pattern-rich data as the environment and framing the data analysis as a survival problem for artificial life. The first generation of agents mastered the pattern discovery task well enough to thrive. Evolution further adapted the agents to their environment by making them a little more pessimistic, and also by making their brains more efficient.

Sustainability. Systems integration for global sustainability.

PubMed

Liu, Jianguo; Mooney, Harold; Hull, Vanessa; Davis, Steven J; Gaskell, Joanne; Hertel, Thomas; Lubchenco, Jane; Seto, Karen C; Gleick, Peter; Kremen, Claire; Li, Shuxin

2015-02-27

Global sustainability challenges, from maintaining biodiversity to providing clean air and water, are closely interconnected yet often separately studied and managed. Systems integration—holistic approaches to integrating various components of coupled human and natural systems—is critical to understand socioeconomic and environmental interconnections and to create sustainability solutions. Recent advances include the development and quantification of integrated frameworks that incorporate ecosystem services, environmental footprints, planetary boundaries, human-nature nexuses, and telecoupling. Although systems integration has led to fundamental discoveries and practical applications, further efforts are needed to incorporate more human and natural components simultaneously, quantify spillover systems and feedbacks, integrate multiple spatial and temporal scales, develop new tools, and translate findings into policy and practice. Such efforts can help address important knowledge gaps, link seemingly unconnected challenges, and inform policy and management decisions. Copyright © 2015, American Association for the Advancement of Science.

Knowledge Discovery/A Collaborative Approach, an Innovative Solution

NASA Technical Reports Server (NTRS)

Fitts, Mary A.

2009-01-01

Collaboration between Medical Informatics and Healthcare Systems (MIHCS) at NASA/Johnson Space Center (JSC) and the Texas Medical Center (TMC) Library was established to investigate technologies for facilitating knowledge discovery across multiple life sciences research disciplines in multiple repositories. After reviewing 14 potential Enterprise Search System (ESS) solutions, Collexis was determined to best meet the expressed needs. A three month pilot evaluation of Collexis produced positive reports from multiple scientists across 12 research disciplines. The joint venture and a pilot-phased approach achieved the desired results without the high cost of purchasing software, hardware or additional resources to conduct the task. Medical research is highly compartmentalized by discipline, e.g. cardiology, immunology, neurology. The medical research community at large, as well as at JSC, recognizes the need for cross-referencing relevant information to generate best evidence. Cross-discipline collaboration at JSC is specifically required to close knowledge gaps affecting space exploration. To facilitate knowledge discovery across these communities, MIHCS combined expertise with the TMC library and found Collexis to best fit the needs of our researchers including:

Big, Deep, and Smart Data in Scanning Probe Microscopy.

PubMed

Kalinin, Sergei V; Strelcov, Evgheni; Belianinov, Alex; Somnath, Suhas; Vasudevan, Rama K; Lingerfelt, Eric J; Archibald, Richard K; Chen, Chaomei; Proksch, Roger; Laanait, Nouamane; Jesse, Stephen

2016-09-27

Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies.

Building Scalable Knowledge Graphs for Earth Science

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Maskey, Manil; Gatlin, Patrick; Zhang, Jia; Duan, Xiaoyi; Miller, J. J.; Bugbee, Kaylin; Christopher, Sundar; Freitag, Brian

2017-01-01

Knowledge Graphs link key entities in a specific domain with other entities via relationships. From these relationships, researchers can query knowledge graphs for probabilistic recommendations to infer new knowledge. Scientific papers are an untapped resource which knowledge graphs could leverage to accelerate research discovery. Goal: Develop an end-to-end (semi) automated methodology for constructing Knowledge Graphs for Earth Science.

Genetic discoveries and nursing implications for complex disease prevention and management.

PubMed

Frazier, Lorraine; Meininger, Janet; Halsey Lea, Dale; Boerwinkle, Eric

2004-01-01

The purpose of this article is to examine the management of patients with complex diseases, in light of recent genetic discoveries, and to explore how these genetic discoveries will impact nursing practice and nursing research. The nursing science processes discussed are not comprehensive of all nursing practice but, instead, are concentrated in areas where genetics will have the greatest influence. Advances in genetic science will revolutionize our approach to patients and to health care in the prevention, diagnosis, and treatment of disease, raising many issues for nursing research and practice. As the scope of genetics expands to encompass multifactorial disease processes, a continuing reexamination of the knowledge base is required for nursing practice, with incorporation of genetic knowledge into the repertoire of every nurse, and with advanced knowledge for nurses who select specialty roles in the genetics area. This article explores the impact of this revolution on nursing science and practice as well as the opportunities for nursing science and practice to participate fully in this revolution. Because of the high proportion of the population at risk for complex diseases and because nurses are occupied every day in the prevention, assessment, treatment, and therapeutic intervention of patients with such diseases in practice and research, there is great opportunity for nurses to improve health care through the application (nursing practice) and discovery (nursing research) of genetic knowledge.

Template-Based Geometric Simulation of Flexible Frameworks

PubMed Central

Wells, Stephen A.; Sartbaeva, Asel

2012-01-01

Specialised modelling and simulation methods implementing simplified physical models are valuable generators of insight. Template-based geometric simulation is a specialised method for modelling flexible framework structures made up of rigid units. We review the background, development and implementation of the method, and its applications to the study of framework materials such as zeolites and perovskites. The “flexibility window” property of zeolite frameworks is a particularly significant discovery made using geometric simulation. Software implementing geometric simulation of framework materials, “GASP”, is freely available to researchers. PMID:28817055

Validating and Modelling Technological Pedagogical Content Knowledge Framework among Asian Preservice Teachers

ERIC Educational Resources Information Center

Chai, Ching Shing; Ng, Eugenia M. W.; Li, Wenhao; Hong, Huang-Yao; Koh, Joyce H. L.

2013-01-01

The Technological Pedagogical Content Knowledge (TPCK) framework has been adopted by many educational technologists and teacher educators for the research and development of knowledge about the pedagogical uses of Information and Communication Technologies (ICT) in classrooms. While the framework is potentially very important, efforts to survey…

Medical data mining: knowledge discovery in a clinical data warehouse.

PubMed Central

Prather, J. C.; Lobach, D. F.; Goodwin, L. K.; Hales, J. W.; Hage, M. L.; Hammond, W. E.

1997-01-01

Clinical databases have accumulated large quantities of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. This paper describes the processes involved in mining a clinical database including data warehousing, data query and cleaning, and data analysis. PMID:9357597

Discovery informatics in biological and biomedical sciences: research challenges and opportunities.

PubMed

Honavar, Vasant

2015-01-01

New discoveries in biological, biomedical and health sciences are increasingly being driven by our ability to acquire, share, integrate and analyze, and construct and simulate predictive models of biological systems. While much attention has focused on automating routine aspects of management and analysis of "big data", realizing the full potential of "big data" to accelerate discovery calls for automating many other aspects of the scientific process that have so far largely resisted automation: identifying gaps in the current state of knowledge; generating and prioritizing questions; designing studies; designing, prioritizing, planning, and executing experiments; interpreting results; forming hypotheses; drawing conclusions; replicating studies; validating claims; documenting studies; communicating results; reviewing results; and integrating results into the larger body of knowledge in a discipline. Against this background, the PSB workshop on Discovery Informatics in Biological and Biomedical Sciences explores the opportunities and challenges of automating discovery or assisting humans in discovery through advances (i) Understanding, formalization, and information processing accounts of, the entire scientific process; (ii) Design, development, and evaluation of the computational artifacts (representations, processes) that embody such understanding; and (iii) Application of the resulting artifacts and systems to advance science (by augmenting individual or collective human efforts, or by fully automating science).

Fishing for Features

ScienceCinema

Heredia-Langner, Alejandro; Cort, John; Bailey, Vanessa

2018-01-16

The Fishing for Features Signature Discovery project developed a framework for discovering signature features in challenging environments involving large and complex data sets or where phenomena may be poorly characterized or understood. Researchers at PNNL have applied the framework to the optimization of biofuels blending and to discover signatures of climate change on microbial soil communities.

Fishing for Features

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

Heredia-Langner, Alejandro; Cort, John; Bailey, Vanessa

2016-07-21

The Fishing for Features Signature Discovery project developed a framework for discovering signature features in challenging environments involving large and complex data sets or where phenomena may be poorly characterized or understood. Researchers at PNNL have applied the framework to the optimization of biofuels blending and to discover signatures of climate change on microbial soil communities.

Building Better Decision-Support by Using Knowledge Discovery.

ERIC Educational Resources Information Center

Jurisica, Igor

2000-01-01

Discusses knowledge-based decision-support systems that use artificial intelligence approaches. Addresses the issue of how to create an effective case-based reasoning system for complex and evolving domains, focusing on automated methods for system optimization and domain knowledge evolution that can supplement knowledge acquired from domain…

Daily life activity routine discovery in hemiparetic rehabilitation patients using topic models.

PubMed

Seiter, J; Derungs, A; Schuster-Amft, C; Amft, O; Tröster, G

2015-01-01

Monitoring natural behavior and activity routines of hemiparetic rehabilitation patients across the day can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. In particular, continuous patient monitoring could add type, frequency and duration of daily life activity routines and hence complement standard clinical scores that are assessed for particular tasks only. Machine learning methods have been applied to infer activity routines from sensor data. However, supervised methods require activity annotations to build recognition models and thus require extensive patient supervision. Discovery methods, including topic models could provide patient routine information and deal with variability in activity and movement performance across patients. Topic models have been used to discover characteristic activity routine patterns of healthy individuals using activity primitives recognized from supervised sensor data. Yet, the applicability of topic models for hemiparetic rehabilitation patients and techniques to derive activity primitives without supervision needs to be addressed. We investigate, 1) whether a topic model-based activity routine discovery framework can infer activity routines of rehabilitation patients from wearable motion sensor data. 2) We compare the performance of our topic model-based activity routine discovery using rule-based and clustering-based activity vocabulary. We analyze the activity routine discovery in a dataset recorded with 11 hemiparetic rehabilitation patients during up to ten full recording days per individual in an ambulatory daycare rehabilitation center using wearable motion sensors attached to both wrists and the non-affected thigh. We introduce and compare rule-based and clustering-based activity vocabulary to process statistical and frequency acceleration features to activity words. Activity words were used for activity routine pattern discovery using topic models based on Latent Dirichlet Allocation. Discovered activity routine patterns were then mapped to six categorized activity routines. Using the rule-based approach, activity routines could be discovered with an average accuracy of 76% across all patients. The rule-based approach outperformed clustering by 10% and showed less confusions for predicted activity routines. Topic models are suitable to discover daily life activity routines in hemiparetic rehabilitation patients without trained classifiers and activity annotations. Activity routines show characteristic patterns regarding activity primitives including body and extremity postures and movement. A patient-independent rule set can be derived. Including expert knowledge supports successful activity routine discovery over completely data-driven clustering.

The Genome-based Knowledge Management in Cycles model: a complex adaptive systems framework for implementation of genomic applications.

PubMed

Arar, Nedal; Knight, Sara J; Modell, Stephen M; Issa, Amalia M

2011-03-01

The main mission of the Genomic Applications in Practice and Prevention Network™ is to advance collaborative efforts involving partners from across the public health sector to realize the promise of genomics in healthcare and disease prevention. We introduce a new framework that supports the Genomic Applications in Practice and Prevention Network mission and leverages the characteristics of the complex adaptive systems approach. We call this framework the Genome-based Knowledge Management in Cycles model (G-KNOMIC). G-KNOMIC proposes that the collaborative work of multidisciplinary teams utilizing genome-based applications will enhance translating evidence-based genomic findings by creating ongoing knowledge management cycles. Each cycle consists of knowledge synthesis, knowledge evaluation, knowledge implementation and knowledge utilization. Our framework acknowledges that all the elements in the knowledge translation process are interconnected and continuously changing. It also recognizes the importance of feedback loops, and the ability of teams to self-organize within a dynamic system. We demonstrate how this framework can be used to improve the adoption of genomic technologies into practice using two case studies of genomic uptake.

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

PubMed Central

McDermott, Jason E.; Wang, Jing; Mitchell, Hugh; Webb-Robertson, Bobbie-Jo; Hafen, Ryan; Ramey, John; Rodland, Karin D.

2012-01-01

Introduction The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers. PMID:23335946

Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system.

PubMed

Boegl, Karl; Adlassnig, Klaus-Peter; Hayashi, Yoichi; Rothenfluh, Thomas E; Leitich, Harald

2004-01-01

This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.

Chapter 1: Biomedical knowledge integration.

PubMed

Payne, Philip R O

2012-01-01

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

Chapter 1: Biomedical Knowledge Integration

PubMed Central

Payne, Philip R. O.

2012-01-01

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems. PMID:23300416

ENERGIC OD Geopan application using Virtual Hub: multi-temporal knowledge oriented information on built environment and riverbed changes to geologist community

NASA Astrophysics Data System (ADS)

Boldrini, E.; Brumana, R.; Previtali, M., Jr.; Mazzetti, P., Sr.; Cuca, B., Sr.; Barazzetti, L., Sr.; Camagni, R.; Santoro, M.

2016-12-01

The Built Environment (BE) is intended as the sum of natural and human activities in dynamic transformations in the past, in the present and in the future: it calls for more informed decisions to face the challenging threats (climate change, natural hazards, anthropic pressures) by exploiting resilience, sustainable intervention and tackling societal opportunities, as heritage valorization and tourism acknowledgment; thus, it asks for awareness rising among circular reflective society. In the framework of ENERGIC OD project (EU Network for Redistributing Geographic Information - Open Data), this paper describes the implementation of an application (GeoPAN Atl@s app) addressed to improve a circular multi-temporal knowledge oriented generation of information, able to integrate and take in account historic and current maps, as well as products of satellite image processing to understand on course and on coming phenomena and relating them with the ones occurred in the ancient and recent past in a diachronic approach. The app is focused on riverbeds-BE and knowledge generation for the detection of their changes by involving geologist community and providing to other user the retrieved information (architects and urban planner, tourists and citizen). Here is described the implementation of the app interfaced with the ENERGIC OD Virtual Hub component, based on a brokering framework for OD discovery and access, to assure interoperability and integration of different datasets, wide spread cartographic products with huge granularity (national, regional environmental Risk Maps, i.e. PAI, on site local data, i.e. UAV data, or results of Copernicus Programme satellite data processing, i.e. object-based and time series image analysis for riverbed monitoring using Sentinel2): different sources, scales and formats, including historical maps needing metadata generation, and SHP data used by the geologist in their daily activities for hydrogeological analysis, to be both usable as OD by the VH. "The research leading to these results has received funding from the European Union ICT Policy Support Programme (ICT PSP) under the Competitiveness and Innovation Framework Programme (CIP), grant agreement n° 620400."

CoINcIDE: A framework for discovery of patient subtypes across multiple datasets.

PubMed

Planey, Catherine R; Gevaert, Olivier

2016-03-09

Patient disease subtypes have the potential to transform personalized medicine. However, many patient subtypes derived from unsupervised clustering analyses on high-dimensional datasets are not replicable across multiple datasets, limiting their clinical utility. We present CoINcIDE, a novel methodological framework for the discovery of patient subtypes across multiple datasets that requires no between-dataset transformations. We also present a high-quality database collection, curatedBreastData, with over 2,500 breast cancer gene expression samples. We use CoINcIDE to discover novel breast and ovarian cancer subtypes with prognostic significance and novel hypothesized ovarian therapeutic targets across multiple datasets. CoINcIDE and curatedBreastData are available as R packages.

Avoiding false discoveries in association studies.

PubMed

Sabatti, Chiara

2007-01-01

We consider the problem of controlling false discoveries in association studies. We assume that the design of the study is adequate so that the "false discoveries" are potentially only because of random chance, not to confounding or other flaws. Under this premise, we review the statistical framework for hypothesis testing and correction for multiple comparisons. We consider in detail the currently accepted strategies in linkage analysis. We then examine the underlying similarities and differences between linkage and association studies and document some of the most recent methodological developments for association mapping.

A Community Roadmap for Discovery of Geosciences Data

NASA Astrophysics Data System (ADS)

Baru, C.

2012-12-01

This talk will summarize on-going discussions and deliberations related to data discovery undertaken as part of the EarthCube initiative and in the context of current trends and technologies in search and discovery of scientific data and information. The goal of the EarthCube initiative is to transform the conduct of research by supporting the development of community-guided cyberinfrastructure to integrate data and information for knowledge management across the Geosciences. The vision of EarthCube is to provide a coherent framework for finding and using information about the Earth system across the entire research enterprise that will allow for substantial improved collaboration between specialties using each other's data (e.g. subdomains of geo- and biological sciences). Indeed, data discovery is an essential prerequisite to any action that an EarthCube user would undertake. The community roadmap activity addresses challenges in data discovery, beginning with an assessment of the state-of-the-art, and then identifying issues, challenges, and risks in reaching the data discovery vision. Many of the lessons learned are general and applicable not only to the geosciences but also to a variety of other science communities. The roadmap considers data discovery issues in Geoscience that include but are not limited to metadata-based discovery and the use of semantic information and ontologies; content-based discovery and integration with data mining activities; integration with data access services; and policy and governance issues. Furthermore, many geoscience use cases require access to heterogeneous data from multiple disciplinary sources in order to analyze and make intelligent connections between data to advance research frontiers. Examples include, say, assessing the rise of sea surface temperatures; modeling geodynamical earth systems from deep time to present; or, examining in detail the causes and consequences of global climate change. It has taken the past one to two decades for the community to arrive at a few commonly understood and commonly agreed upon standards for metadata and services. There have been significant advancements in the development of prototype systems in the area of metadata-based data discovery, including efforts such as OpenDAP and THREDDS catalogs, the GEON Portal and Catalog Services (www.geongrid.org), OGC standards, and development of systems like OneGeology (onegeology.org), the USGIN (usgin.org), the Earth System Grid, and EOSDIS. Such efforts have set the stage now for the development of next generation, production-quality, advanced discovery services. The next challenge is in converting these into robust, sustained services for the community and developing capabilities such as content-based search and ontology-enabled search, and ensuring that the long tail of geoscience data are fully included in any future discovery services. As EarthCube attempts to pursue these challenges, the key question to pose is whether we will be able to establish a cultural environment that is able to sustain, extend, and manage an infrastructure that will last 50, 100 years?

A neotropical Miocene pollen database employing image-based search and semantic modeling.

PubMed

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W; Jaramillo, Carlos; Shyu, Chi-Ren

2014-08-01

Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.

Metal-Organic-Framework-Derived Carbon Nanostructure Augmented Sonodynamic Cancer Therapy.

PubMed

Pan, Xueting; Bai, Lixin; Wang, Hui; Wu, Qingyuan; Wang, Hongyu; Liu, Shuang; Xu, Bolong; Shi, Xinghua; Liu, Huiyu

2018-06-01

Sonodynamic therapy (SDT) can overcome the critical issue of depth-penetration barrier of photo-triggered therapeutic modalities. However, the discovery of sonosensitizers with high sonosensitization efficacy and good stability is still a significant challenge. In this study, the great potential of a metal-organic-framework (MOF)-derived carbon nanostructure that contains porphyrin-like metal centers (PMCS) to act as an excellent sonosensitizer is identified. Excitingly, the superior sonosensitization effect of PMCS is believed to be closely linked to the porphyrin-like macrocycle in MOF-derived nanostructure in comparison to amorphous carbon nanospheres, due to their large highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap for high reactive oxygen species (ROS) production. The nanoparticle-assisted cavitation process, including the visualized formation of the cavitation bubbles and microjets, is also first captured by high-speed camera. High ROS production in PMCS under ultrasound is validated by electron spin resonance and dye measurement, followed by cellular destruction and high tumor inhibition efficiency (85%). This knowledge is important from the perspective of understanding the structure-dependent SDT enhancement of a MOF-derived carbon nanostructure. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition

NASA Astrophysics Data System (ADS)

Wang, Qian; Gosik, Kirk; Xing, Sujuan; Jiang, Libo; Sun, Lidan; Chinchilli, Vernon M.; Wu, Rongling

2017-03-01

Epigenetic reprogramming is thought to play a critical role in maintaining the normal development of embryos. How the methylation state of paternal and maternal genomes regulates embryogenesis depends on the interaction and coordination of the gametes of two sexes. While there is abundant research in exploring the epigenetic interactions of sperms and oocytes, a knowledge gap exists in the mechanistic quantitation of these interactions and their impact on embryo development. This review aims at formulating a modeling framework to address this gap through the integration and synthesis of evolutionary game theory and the latest discoveries of the epigenetic control of embryo development by next-generation sequencing. This framework, named epigenetic game theory or epiGame, views embryogenesis as an ecological system in which two highly distinct and specialized gametes coordinate through either cooperation or competition, or both, to maximize the fitness of embryos under Darwinian selection. By implementing a system of ordinary differential equations, epiGame quantifies the pattern and relative magnitude of the methylation effects on embryogenesis by the mechanisms of cooperation and competition. epiGame may gain new insight into reproductive biology and can be potentially applied to design personalized medicines for genetic disorder intervention.

Redefining responsible research and innovation for the advancement of biobanking and biomedical research.

PubMed

Yu, Helen

2016-12-01

One of the core objectives of responsible research and innovation (RRI) is to maximize the value of publicly funded research so that it may be returned to benefit society. However, while RRI encourages innovation through societal engagement, it can give rise to complex and previously untested issues that challenge the existing legal frameworks on intellectual property (IP) and public entitlement to benefits of research. In the case of biobanking, the personal nature of human biological materials and often altruistic intention of participants to donate samples intensifies the need to adhere to RRI principles with respect to the research, development, and commercialization of innovations derived from biobanks. However, stakeholders participate and collaborate with others in the innovation process to fulfill their own agenda. Without IP to safeguard investments in R&D, stakeholders may hesitate to contribute to the translation of discoveries into innovations. To realize the public benefit objective, RRI principles must protect the interests of stakeholders involved in the translation and commercialization of knowledge. This article explores the seemingly contradictory and competing objectives of open science and commercialization and proposes a holistic innovation framework directed at improving RRI practice for positive impact on obtaining the optimal social and economic values from research.

Interspecific social networks promote information transmission in wild songbirds.

PubMed

Farine, Damien R; Aplin, Lucy M; Sheldon, Ben C; Hoppitt, William

2015-03-22

Understanding the functional links between social structure and population processes is a central aim of evolutionary ecology. Multiple types of interactions can be represented by networks drawn for the same population, such as kinship, dominance or affiliative networks, but the relative importance of alternative networks in modulating population processes may not be clear. We illustrate this problem, and a solution, by developing a framework for testing the importance of different types of association in facilitating the transmission of information. We apply this framework to experimental data from wild songbirds that form mixed-species flocks, recording the arrival (patch discovery) of individuals to novel foraging sites. We tested whether intraspecific and interspecific social networks predicted the spread of information about novel food sites, and found that both contributed to transmission. The likelihood of acquiring information per unit of connection to knowledgeable individuals increased 22-fold for conspecifics, and 12-fold for heterospecifics. We also found that species varied in how much information they produced, suggesting that some species play a keystone role in winter foraging flocks. More generally, these analyses demonstrate that this method provides a powerful approach, using social networks to quantify the relative transmission rates across different social relationships.

Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: an informatics framework for materials design.

PubMed

Srinivasan, Srikant; Broderick, Scott R; Zhang, Ruifeng; Mishra, Amrita; Sinnott, Susan B; Saxena, Surendra K; LeBeau, James M; Rajan, Krishna

2015-12-18

A data driven methodology is developed for tracking the collective influence of the multiple attributes of alloying elements on both thermodynamic and mechanical properties of metal alloys. Cobalt-based superalloys are used as a template to demonstrate the approach. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can guide targeted first principles calculations that identify the influence of specific elements on phase stability, crystal structure and elastic properties. This provides a fundamentally new means to rapidly identify new stable alloy chemistries with enhanced high temperature properties. The resulting visualization scheme exhibits the grouping and proximity of elements based on their impact on the properties of intermetallic alloys. Unlike the periodic table however, the distance between neighboring elements uncovers relationships in a complex high dimensional information space that would not have been easily seen otherwise. The predictions of the methodology are found to be consistent with reported experimental and theoretical studies. The informatics based methodology presented in this study can be generalized to a framework for data analysis and knowledge discovery that can be applied to many material systems and recreated for different design objectives.

Toward a Conceptual Knowledge Management Framework in Health

PubMed Central

Lau, Francis

2004-01-01

This paper describes a conceptual organizing scheme for managing knowledge within the health setting. First, a brief review of the notions of knowledge and knowledge management is provided. This is followed by a detailed depiction of our proposed knowledge management framework, which focuses on the concepts of production, use, and refinement of three specific knowledge sources-policy, evidence, and experience. These concepts are operationalized through a set of knowledge management methods and tools tailored for the health setting. We include two case studies around knowledge translation on parent-child relations and virtual networks in community health research to illustrate how this knowledge management framework can be operationalized within specific contexts and the issues involved. We conclude with the lessons learned and implications. PMID:18066388

A two-step hierarchical hypothesis set testing framework, with applications to gene expression data on ordered categories

PubMed Central

2014-01-01

Background In complex large-scale experiments, in addition to simultaneously considering a large number of features, multiple hypotheses are often being tested for each feature. This leads to a problem of multi-dimensional multiple testing. For example, in gene expression studies over ordered categories (such as time-course or dose-response experiments), interest is often in testing differential expression across several categories for each gene. In this paper, we consider a framework for testing multiple sets of hypothesis, which can be applied to a wide range of problems. Results We adopt the concept of the overall false discovery rate (OFDR) for controlling false discoveries on the hypothesis set level. Based on an existing procedure for identifying differentially expressed gene sets, we discuss a general two-step hierarchical hypothesis set testing procedure, which controls the overall false discovery rate under independence across hypothesis sets. In addition, we discuss the concept of the mixed-directional false discovery rate (mdFDR), and extend the general procedure to enable directional decisions for two-sided alternatives. We applied the framework to the case of microarray time-course/dose-response experiments, and proposed three procedures for testing differential expression and making multiple directional decisions for each gene. Simulation studies confirm the control of the OFDR and mdFDR by the proposed procedures under independence and positive correlations across genes. Simulation results also show that two of our new procedures achieve higher power than previous methods. Finally, the proposed methodology is applied to a microarray dose-response study, to identify 17 β-estradiol sensitive genes in breast cancer cells that are induced at low concentrations. Conclusions The framework we discuss provides a platform for multiple testing procedures covering situations involving two (or potentially more) sources of multiplicity. The framework is easy to use and adaptable to various practical settings that frequently occur in large-scale experiments. Procedures generated from the framework are shown to maintain control of the OFDR and mdFDR, quantities that are especially relevant in the case of multiple hypothesis set testing. The procedures work well in both simulations and real datasets, and are shown to have better power than existing methods. PMID:24731138

Automated vocabulary discovery for geo-parsing online epidemic intelligence.

PubMed

Keller, Mikaela; Freifeld, Clark C; Brownstein, John S

2009-11-24

Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human. Given that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon. The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting.

The Biomedical Resource Ontology (BRO) to Enable Resource Discovery in Clinical and Translational Research

PubMed Central

Tenenbaum, Jessica D.; Whetzel, Patricia L.; Anderson, Kent; Borromeo, Charles D.; Dinov, Ivo D.; Gabriel, Davera; Kirschner, Beth; Mirel, Barbara; Morris, Tim; Noy, Natasha; Nyulas, Csongor; Rubenson, David; Saxman, Paul R.; Singh, Harpreet; Whelan, Nancy; Wright, Zach; Athey, Brian D.; Becich, Michael J.; Ginsburg, Geoffrey S.; Musen, Mark A.; Smith, Kevin A.; Tarantal, Alice F.; Rubin, Daniel L; Lyster, Peter

2010-01-01

The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health. PMID:20955817

Orphan diseases: state of the drug discovery art.

PubMed

Volmar, Claude-Henry; Wahlestedt, Claes; Brothers, Shaun P

2017-06-01

Since 1983 more than 300 drugs have been developed and approved for orphan diseases. However, considering the development of novel diagnosis tools, the number of rare diseases vastly outpaces therapeutic discovery. Academic centers and nonprofit institutes are now at the forefront of rare disease R&D, partnering with pharmaceutical companies when academic researchers discover novel drugs or targets for specific diseases, thus reducing the failure risk and cost for pharmaceutical companies. Considerable progress has occurred in the art of orphan drug discovery, and a symbiotic relationship now exists between pharmaceutical industry, academia, and philanthropists that provides a useful framework for orphan disease therapeutic discovery. Here, the current state-of-the-art of drug discovery for orphan diseases is reviewed. Current technological approaches and challenges for drug discovery are considered, some of which can present somewhat unique challenges and opportunities in orphan diseases, including the potential for personalized medicine, gene therapy, and phenotypic screening.

To ontologise or not to ontologise: An information model for a geospatial knowledge infrastructure

NASA Astrophysics Data System (ADS)

Stock, Kristin; Stojanovic, Tim; Reitsma, Femke; Ou, Yang; Bishr, Mohamed; Ortmann, Jens; Robertson, Anne

2012-08-01

A geospatial knowledge infrastructure consists of a set of interoperable components, including software, information, hardware, procedures and standards, that work together to support advanced discovery and creation of geoscientific resources, including publications, data sets and web services. The focus of the work presented is the development of such an infrastructure for resource discovery. Advanced resource discovery is intended to support scientists in finding resources that meet their needs, and focuses on representing the semantic details of the scientific resources, including the detailed aspects of the science that led to the resource being created. This paper describes an information model for a geospatial knowledge infrastructure that uses ontologies to represent these semantic details, including knowledge about domain concepts, the scientific elements of the resource (analysis methods, theories and scientific processes) and web services. This semantic information can be used to enable more intelligent search over scientific resources, and to support new ways to infer and visualise scientific knowledge. The work describes the requirements for semantic support of a knowledge infrastructure, and analyses the different options for information storage based on the twin goals of semantic richness and syntactic interoperability to allow communication between different infrastructures. Such interoperability is achieved by the use of open standards, and the architecture of the knowledge infrastructure adopts such standards, particularly from the geospatial community. The paper then describes an information model that uses a range of different types of ontologies, explaining those ontologies and their content. The information model was successfully implemented in a working geospatial knowledge infrastructure, but the evaluation identified some issues in creating the ontologies.

Genomics and transcriptomics in drug discovery.

PubMed

Dopazo, Joaquin

2014-02-01

The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery. Copyright © 2013 Elsevier Ltd. All rights reserved.

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.

PubMed

Hassani-Pak, Keywan; Rawlings, Christopher

2017-06-13

Genetics and "omics" studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.

Estimating the rate of biological introductions: Lessepsian fishes in the Mediterranean.

PubMed

Belmaker, Jonathan; Brokovich, Eran; China, Victor; Golani, Daniel; Kiflawi, Moshe

2009-04-01

Sampling issues preclude the direct use of the discovery rate of exotic species as a robust estimate of their rate of introduction. Recently, a method was advanced that allows maximum-likelihood estimation of both the observational probability and the introduction rate from the discovery record. Here, we propose an alternative approach that utilizes the discovery record of native species to control for sampling effort. Implemented in a Bayesian framework using Markov chain Monte Carlo simulations, the approach provides estimates of the rate of introduction of the exotic species, and of additional parameters such as the size of the species pool from which they are drawn. We illustrate the approach using Red Sea fishes recorded in the eastern Mediterranean, after crossing the Suez Canal, and show that the two approaches may lead to different conclusions. The analytical framework is highly flexible and could provide a basis for easy modification to other systems for which first-sighting data on native and introduced species are available.

Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

ERIC Educational Resources Information Center

Chen, Wei-Bang

2012-01-01

The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

Computer-aided drug design at Boehringer Ingelheim

NASA Astrophysics Data System (ADS)

Muegge, Ingo; Bergner, Andreas; Kriegl, Jan M.

2017-03-01

Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.

Great Originals of Modern Physics

ERIC Educational Resources Information Center

Decker, Fred W.

1972-01-01

European travel can provide an intimate view of the implements and locales of great discoveries in physics for the knowledgeable traveler. The four museums at Cambridge, London, Remscheid-Lennep, and Munich display a full range of discovery apparatus in modern physics as outlined here. (Author/TS)

Dulse on the Distaff Side.

ERIC Educational Resources Information Center

MacKenzie, Marion

1983-01-01

Scientific research leading to the discovery of female plants of the red alga Palmaria plamata (dulse) is described. This discovery has not only advanced knowledge of marine organisms and taxonomic relationships but also has practical implications. The complete life cycle of this organism is included. (JN)

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature, custody... persons having knowledge of any discoverable matter. (b) It is not ground for objection that information...

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature, custody... persons having knowledge of any discoverable matter. (b) It is not ground for objection that information...

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature, custody... persons having knowledge of any discoverable matter. (b) It is not ground for objection that information...

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature, custody... persons having knowledge of any discoverable matter. (b) It is not ground for objection that information...

Development of a mentorship strategy: a knowledge translation case study.

PubMed

Straus, Sharon E; Graham, Ian D; Taylor, Mark; Lockyer, Jocelyn

2008-01-01

There are many theories and frameworks for achieving knowledge translation, and the assortment can be confusing to those responsible for planning, evaluation, or policymaking in knowledge translation. A conceptual framework developed by Graham and colleagues provides an approach that builds on the commonalities found in an assessment of planned-action theories. This article describes the application of this knowledge to action framework to a mentorship initiative in academic medicine. Mentorship influences career success but is threatened in academia by increased clinical, research, and administrative demands. A case study review was undertaken of the role of mentors, the experiences of mentors and mentees, and mentorship initiatives in developing and retaining clinician scientists at two universities in Alberta, Canada. This project involved relevant stakeholders including researchers, university administrators, and research funders. The knowledge to action framework was used to develop a strategy for mentorship for clinician researchers. The framework highlights the need to identify and engage stakeholders in the process of knowledge implementation. A series of initiatives were selected and tailored to barriers and facilitators to implementation of the mentorship initiative; strategies for evaluating the knowledge use and its impact on outcomes were developed. The knowledge to action framework can be used to develop a mentorship initiative for clinician researchers. Future work to evaluate the impact of this intervention on recruitment and retention is planned.

Scientific Knowledge Discovery in Complex Semantic Networks of Geophysical Systems

NASA Astrophysics Data System (ADS)

Fox, P.

2012-04-01

The vast majority of explorations of the Earth's systems are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. Recent successes in the application of complex network theory and algorithms to climate data, raise expectations that more general graph-based approaches offer the opportunity for new discoveries. In the past ~ 5 years in the natural sciences there has substantial progress in providing both specialists and non-specialists the ability to describe in machine readable form, geophysical quantities and relations among them in meaningful and natural ways, effectively breaking the prior syntax barrier. The corresponding open-world semantics and reasoning provide higher-level interconnections. That is, semantics provided around the data structures, using semantically-equipped tools, and semantically aware interfaces between science application components allowing for discovery at the knowledge level. More recently, formal semantic approaches to continuous and aggregate physical processes are beginning to show promise and are soon likely to be ready to apply to geoscientific systems. To illustrate these opportunities, this presentation presents two application examples featuring domain vocabulary (ontology) and property relations (named and typed edges in the graphs). First, a climate knowledge discovery pilot encoding and exploration of CMIP5 catalog information with the eventual goal to encode and explore CMIP5 data. Second, a multi-stakeholder knowledge network for integrated assessments in marine ecosystems, where the data is highly inter-disciplinary.

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications.

PubMed

Kasabov, Nikola; Scott, Nathan Matthew; Tu, Enmei; Marks, Stefan; Sengupta, Neelava; Capecci, Elisa; Othman, Muhaini; Doborjeh, Maryam Gholami; Murli, Norhanifah; Hartono, Reggio; Espinosa-Ramos, Josafath Israel; Zhou, Lei; Alvi, Fahad Bashir; Wang, Grace; Taylor, Denise; Feigin, Valery; Gulyaev, Sergei; Mahmoud, Mahmoud; Hou, Zeng-Guang; Yang, Jie

2016-06-01

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM. Copyright © 2015 Elsevier Ltd. All rights reserved.

Key principles for a national clinical decision support knowledge sharing framework: synthesis of insights from leading subject matter experts.

PubMed

Kawamoto, Kensaku; Hongsermeier, Tonya; Wright, Adam; Lewis, Janet; Bell, Douglas S; Middleton, Blackford

2013-01-01

To identify key principles for establishing a national clinical decision support (CDS) knowledge sharing framework. As part of an initiative by the US Office of the National Coordinator for Health IT (ONC) to establish a framework for national CDS knowledge sharing, key stakeholders were identified. Stakeholders' viewpoints were obtained through surveys and in-depth interviews, and findings and relevant insights were summarized. Based on these insights, key principles were formulated for establishing a national CDS knowledge sharing framework. Nineteen key stakeholders were recruited, including six executives from electronic health record system vendors, seven executives from knowledge content producers, three executives from healthcare provider organizations, and three additional experts in clinical informatics. Based on these stakeholders' insights, five key principles were identified for effectively sharing CDS knowledge nationally. These principles are (1) prioritize and support the creation and maintenance of a national CDS knowledge sharing framework; (2) facilitate the development of high-value content and tooling, preferably in an open-source manner; (3) accelerate the development or licensing of required, pragmatic standards; (4) acknowledge and address medicolegal liability concerns; and (5) establish a self-sustaining business model. Based on the principles identified, a roadmap for national CDS knowledge sharing was developed through the ONC's Advancing CDS initiative. The study findings may serve as a useful guide for ongoing activities by the ONC and others to establish a national framework for sharing CDS knowledge and improving clinical care.

Beginning to manage drug discovery and development knowledge.

PubMed

Sumner-Smith, M

2001-05-01

Knowledge management approaches and technologies are beginning to be implemented by the pharmaceutical industry in support of new drug discovery and development processes aimed at greater efficiencies and effectiveness. This trend coincides with moves to reduce paper, coordinate larger teams with more diverse skills that are distributed around the globe, and to comply with regulatory requirements for electronic submissions and the associated maintenance of electronic records. Concurrently, the available technologies have implemented web-based architectures with a greater range of collaborative tools and personalization through portal approaches. However, successful application of knowledge management methods depends on effective cultural change management, as well as proper architectural design to match the organizational and work processes within a company.

Incorporating Resilience into Dynamic Social Models

DTIC Science & Technology

2016-07-20

solved by simply using the information provided by the scenario. Instead, additional knowledge is required from relevant fields that study these...resilience function by leveraging Bayesian Knowledge Bases (BKBs), a probabilistic reasoning network framework[5],[6]. BKBs allow for inferencing...reasoning network framework based on Bayesian Knowledge Bases (BKBs). BKBs are central to our social resilience framework as they are used to

Exceptional responders in conservation.

PubMed

Post, Gerald; Geldmann, Jonas

2017-08-30

Conservation operates within complex systems with incomplete knowledge of the system and the interventions utilized. This frequently results in the inability to find generally applicable methods to alleviate threats to Earth's vanishing wildlife. One approach used in medicine and the social sciences has been to develop a deeper understanding of positive outliers. Where such outliers share similar characteristics, they may be considered exceptional responders. We devised a 4-step framework for identifying exceptional responders in conservation: identification of the study system, identification of the response structure, identification of the threshold for exceptionalism, and identification of commonalities among outliers. Evaluation of exceptional responders provides additional information that is often ignored in randomized controlled trials and before-after control-intervention experiments. Interrogating the contextual factors that contribute to an exceptional outcome allow exceptional responders to become valuable pieces of information leading to unexpected discoveries and novel hypotheses. © 2017 Society for Conservation Biology.

The antibiotic resistome.

PubMed

Wright, Gerard D

2010-08-01

Antibiotics are essential for the treatment of bacterial infections and are among our most important drugs. Resistance has emerged to all classes of antibiotics in clinical use. Antibiotic resistance has, proven inevitable and very often it emerges rapidly after the introduction of a drug into the clinic. There is, therefore, a great interest in understanding the origins, scope and evolution of antibiotic resistance. The review discusses the concept of the antibiotic resistome, which is the collection of all genes that directly or indirectly contribute to antibiotic resistance. The review seeks to assemble current knowledge of the resistome concept as a means of understanding the totality of resistance and not just resistance in pathogenic bacteria. The concept of the antibiotic resistome provides a framework for the study and understanding of how resistance emerges and evolves. Furthermore, the study of the resistome reveals strategies that can be applied in new antibiotic discoveries.

Pacific Research Platform - Creation of a West Coast Big Data Freeway System Applied to the CONNected objECT (CONNECT) Data Mining Framework for Earth Science Knowledge Discovery

NASA Astrophysics Data System (ADS)

Sellars, S. L.; Nguyen, P.; Tatar, J.; Graham, J.; Kawsenuk, B.; DeFanti, T.; Smarr, L.; Sorooshian, S.; Ralph, M.

2017-12-01

A new era in computational earth sciences is within our grasps with the availability of ever-increasing earth observational data, enhanced computational capabilities, and innovative computation approaches that allow for the assimilation, analysis and ability to model the complex earth science phenomena. The Pacific Research Platform (PRP), CENIC and associated technologies such as the Flash I/O Network Appliance (FIONA) provide scientists a unique capability for advancing towards this new era. This presentation reports on the development of multi-institutional rapid data access capabilities and data pipeline for applying a novel image characterization and segmentation approach, CONNected objECT (CONNECT) algorithm to study Atmospheric River (AR) events impacting the Western United States. ARs are often associated with torrential rains, swollen rivers, flash flooding, and mudslides. CONNECT is computationally intensive, reliant on very large data transfers, storage and data mining techniques. The ability to apply the method to multiple variables and datasets located at different University of California campuses has previously been challenged by inadequate network bandwidth and computational constraints. The presentation will highlight how the inter-campus CONNECT data mining framework improved from our prior download speeds of 10MB/s to 500MB/s using the PRP and the FIONAs. We present a worked example using the NASA MERRA data to describe how the PRP and FIONA have provided researchers with the capability for advancing knowledge about ARs. Finally, we will discuss future efforts to expand the scope to additional variables in earth sciences.

A case-study of a socio-scientific issues curricular and pedagogical intervention in an undergraduate microbiology course: A focus on informal reasoning

NASA Astrophysics Data System (ADS)

Schalk, Kelly A.

The purpose of this investigation was to measure specific ways a student interest SSI-based curricular and pedagogical affects undergraduates' ability informally reason. The delimited components of informal reasoning measured were undergraduates' Nature of Science conceptualizations and ability to evaluate scientific information. The socio-scientific issues (SSI) theoretical framework used in this case-study has been advocated as a means for improving students' functional scientific literacy. This investigation focused on the laboratory component of an undergraduate microbiology course in spring 2008. There were 26 participants. The instruments used in this study included: (1) Individual and Group research projects, (2) journals, (3) laboratory write-ups, (4) a laboratory quiz, (5) anonymous evaluations, and (6) a pre/post article exercise. All instruments yielded qualitative data, which were coded using the qualitative software NVivo7. Data analyses were subjected to instrumental triangulation, inter-rater reliability, and member-checking. It was determined that undergraduates' epistemological knowledge of scientific discovery, processes, and justification matured in response to the intervention. Specifically, students realized: (1) differences between facts, theories, and opinions; (2) testable questions are not definitively proven; (3) there is no stepwise scientific process; and (4) lack of data weakens a claim. It was determined that this knowledge influenced participants' beliefs and ability to informally reason. For instance, students exhibited more critical evaluations of scientific information. It was also found that undergraduates' prior opinions had changed over the semester. Further, the student interest aspect of this framework engaged learners by offering participants several opportunities to influentially examine microbiology issues that affected their life. The investigation provided empirically based insights into the ways undergraduates' interest and functional scientific literacy can be promoted. The investigation advanced what was known about using SSI-based frameworks to the post-secondary learner context. Outstanding questions remain for investigation. For example, is this type of student interest SSI-based intervention broadly applicable (i.e., in other science disciplines and grade levels)? And, what challenges would teachers in diverse contexts encounter when implementing a SSI-based theoretical framework?

From Information Center to Discovery System: Next Step for Libraries?

ERIC Educational Resources Information Center

Marcum, James W.

2001-01-01

Proposes a discovery system model to guide technology integration in academic libraries that fuses organizational learning, systems learning, and knowledge creation techniques with constructivist learning practices to suggest possible future directions for digital libraries. Topics include accessing visual and continuous media; information…

Foreword to "The Secret of Childhood."

ERIC Educational Resources Information Center

Stephenson, Margaret E.

2000-01-01

Discusses the basic discoveries of Montessori's Casa dei Bambini. Considers principles of Montessori's organizing theory: the absorbent mind, the unfolding nature of life, the spiritual embryo, self-construction, acquisition of culture, creativity of life, repetition of exercise, freedom within limits, children's discovery of knowledge, the secret…

Cosmic Discovery

NASA Astrophysics Data System (ADS)

Harwit, Martin

1984-04-01

In the remarkable opening section of this book, a well-known Cornell astronomer gives precise thumbnail histories of the 43 basic cosmic discoveries - stars, planets, novae, pulsars, comets, gamma-ray bursts, and the like - that form the core of our knowledge of the universe. Many of them, he points out, were made accidentally and outside the mainstream of astronomical research and funding. This observation leads him to speculate on how many more major phenomena there might be and how they might be most effectively sought out in afield now dominated by large instruments and complex investigative modes and observational conditions. The book also examines discovery in terms of its political, financial, and sociological context - the role of new technologies and of industry and the military in revealing new knowledge; and methods of funding, of peer review, and of allotting time on our largest telescopes. It concludes with specific recommendations for organizing astronomy in ways that will best lead to the discovery of the many - at least sixty - phenomena that Harwit estimates are still waiting to be found.

The discovery of medicines for rare diseases

PubMed Central

Swinney, David C; Xia, Shuangluo

2015-01-01

There is a pressing need for new medicines (new molecular entities; NMEs) for rare diseases as few of the 6800 rare diseases (according to the NIH) have approved treatments. Drug discovery strategies for the 102 orphan NMEs approved by the US FDA between 1999 and 2012 were analyzed to learn from past success: 46 NMEs were first in class; 51 were followers; and five were imaging agents. First-in-class medicines were discovered with phenotypic assays (15), target-based approaches (12) and biologic strategies (18). Identification of genetic causes in areas with more basic and translational research such as cancer and in-born errors in metabolism contributed to success regardless of discovery strategy. In conclusion, greater knowledge increases the chance of success and empirical solutions can be effective when knowledge is incomplete. PMID:25068983

The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

PubMed Central

2014-01-01

The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org. PMID:24602174

An introduction to the multisystem model of knowledge integration and translation.

PubMed

Palmer, Debra; Kramlich, Debra

2011-01-01

Many nurse researchers have designed strategies to assist health care practitioners to move evidence into practice. While many have been identified as "models," most do not have a conceptual framework. They are unidirectional, complex, and difficult for novice research users to understand. These models have focused on empirical knowledge and ignored the importance of practitioners' tacit knowledge. The Communities of Practice conceptual framework allows for the integration of tacit and explicit knowledge into practice. This article describes the development of a new translation model, the Multisystem Model of Knowledge Integration and Translation, supported by the Communities of Practice conceptual framework.

Translational Scholarship and a Palliative Approach: Enlisting the Knowledge-As-Action Framework.

PubMed

Reimer-Kirkham, Sheryl; Doane, Gweneth Hartrick; Antifeau, Elisabeth; Pesut, Barbara; Porterfield, Pat; Roberts, Della; Stajduhar, Kelli; Wikjord, Nicole

2015-01-01

Based on a retheorized epistemology for knowledge translation (KT) that problematizes the "know-do gap" and conceptualizes the knower, knowledge, and action as inseparable, this paper describes the application of the Knowledge-As-Action Framework. When applied as a heuristic device to support an inquiry process, the framework with the metaphor of a kite facilitates a responsiveness to the complexities that characterize KT. Examples from a KT demonstration project on the integration of a palliative approach at 3 clinical sites illustrate the interrelatedness of 6 dimensions-the local context, processes, people, knowledge, fluctuating realities, and values.

Perspectives on Pre-Service Teacher Knowledge for Teaching Early Algebra

ERIC Educational Resources Information Center

McAuliffe, Sharon; Lubben, Fred

2013-01-01

This paper examines a pre-service teacher's content knowledge for teaching early algebra from two perspectives, i.e. using "Rowland's Knowledge Quartet" theory and "Ball's framework for Mathematical Knowledge for Testing" (MKfT). The study intends to examine the differences between the influences using each framework and to…

A Review of Technological Pedagogical Content Knowledge

ERIC Educational Resources Information Center

Chai, Ching Sing; Koh, Joyce Hwee Ling; Tsai, Chin-Chung

2013-01-01

This paper reviews 74 journal papers that investigate ICT integration from the framework of technological pedagogical content knowledge (TPACK). The TPACK framework is an extension of the pedagogical content knowledge (Shulman, 1986). TPACK is the type of integrative and transformative knowledge teachers need for effective use of ICT in…

Teacher Knowledge: A Complex Tapestry

ERIC Educational Resources Information Center

Adoniou, Misty

2015-01-01

Teachers need to know a great deal, in many areas and in multiple ways. Teacher knowledge is a complex tapestry, and teachers must successfully weave the multiple threads. In this article, I present a conceptualisation of teacher knowledge that provides a framework for describing the complexity of teacher knowledge. The framework describes three…

Co-production of knowledge-action systems in urban sustainable governance: The KASA approach

Treesearch

T.A. Munoz-Erickson

2014-01-01

This paper examines how knowledge-action-systems the networks of actors involved in the production, sharing and use of policy-relevant knowledge - work in the process of developing sustainable strategies for cities. I developed an interdisciplinary framework- the knowledge-action system analysis (KASA) framework ...

Data Warehouse Discovery Framework: The Foundation

NASA Astrophysics Data System (ADS)

Apanowicz, Cas

The cost of building an Enterprise Data Warehouse Environment runs usually in millions of dollars and takes years to complete. The cost, as big as it is, is not the primary problem for a given corporation. The risk that all money allocated for planning, design and implementation of the Data Warehouse and Business Intelligence Environment may not bring the result expected, fare out way the cost of entire effort [2,10]. The combination of the two above factors is the main reason that Data Warehouse/Business Intelligence is often single most expensive and most risky IT endeavor for companies [13]. That situation was the main author's inspiration behind founding of Infobright Corp and later on the concept of Data Warehouse Discovery Framework.

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks

PubMed Central

Valdivieso Caraguay, Ángel Leonardo; García Villalba, Luis Javier

2017-01-01

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors. PMID:28362346

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks.

PubMed

Caraguay, Ángel Leonardo Valdivieso; Villalba, Luis Javier García

2017-03-31

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors.

Knowledge discovery from structured mammography reports using inductive logic programming.

PubMed

Burnside, Elizabeth S; Davis, Jesse; Costa, Victor Santos; Dutra, Inês de Castro; Kahn, Charles E; Fine, Jason; Page, David

2005-01-01

The development of large mammography databases provides an opportunity for knowledge discovery and data mining techniques to recognize patterns not previously appreciated. Using a database from a breast imaging practice containing patient risk factors, imaging findings, and biopsy results, we tested whether inductive logic programming (ILP) could discover interesting hypotheses that could subsequently be tested and validated. The ILP algorithm discovered two hypotheses from the data that were 1) judged as interesting by a subspecialty trained mammographer and 2) validated by analysis of the data itself.

A Metadata based Knowledge Discovery Methodology for Seeding Translational Research.

PubMed

Kothari, Cartik R; Payne, Philip R O

2015-01-01

In this paper, we present a semantic, metadata based knowledge discovery methodology for identifying teams of researchers from diverse backgrounds who can collaborate on interdisciplinary research projects: projects in areas that have been identified as high-impact areas at The Ohio State University. This methodology involves the semantic annotation of keywords and the postulation of semantic metrics to improve the efficiency of the path exploration algorithm as well as to rank the results. Results indicate that our methodology can discover groups of experts from diverse areas who can collaborate on translational research projects.

Understanding the Theoretical Framework of Technological Pedagogical Content Knowledge: A Collaborative Self-Study to Understand Teaching Practice and Aspects of Knowledge

ERIC Educational Resources Information Center

Fransson, Goran; Holmberg, Jorgen

2012-01-01

This paper describes a self-study research project that focused on our experiences when planning, teaching, and evaluating a course in initial teacher education. The theoretical framework of technological pedagogical content knowledge (TPACK) was used as a conceptual structure for the self-study. Our understanding of the framework in relation to…

How Conceptual Frameworks Influence Discovery and Depictions of Emotions in Clinical Relationships

ERIC Educational Resources Information Center

Duchan, Judith Felson

2011-01-01

Although emotions are often seen as key to maintaining rapport between speech-language pathologists and their clients, they are often neglected in the research and clinical literature. This neglect, it is argued here, comes in part from the inadequacies of prevailing conceptual frameworks used to govern practices. I aim to show how six such…

The self-organizing fractal theory as a universal discovery method: the phenomenon of life.

PubMed

Kurakin, Alexei

2011-03-29

A universal discovery method potentially applicable to all disciplines studying organizational phenomena has been developed. This method takes advantage of a new form of global symmetry, namely, scale-invariance of self-organizational dynamics of energy/matter at all levels of organizational hierarchy, from elementary particles through cells and organisms to the Universe as a whole. The method is based on an alternative conceptualization of physical reality postulating that the energy/matter comprising the Universe is far from equilibrium, that it exists as a flow, and that it develops via self-organization in accordance with the empirical laws of nonequilibrium thermodynamics. It is postulated that the energy/matter flowing through and comprising the Universe evolves as a multiscale, self-similar structure-process, i.e., as a self-organizing fractal. This means that certain organizational structures and processes are scale-invariant and are reproduced at all levels of the organizational hierarchy. Being a form of symmetry, scale-invariance naturally lends itself to a new discovery method that allows for the deduction of missing information by comparing scale-invariant organizational patterns across different levels of the organizational hierarchy.An application of the new discovery method to life sciences reveals that moving electrons represent a keystone physical force (flux) that powers, animates, informs, and binds all living structures-processes into a planetary-wide, multiscale system of electron flow/circulation, and that all living organisms and their larger-scale organizations emerge to function as electron transport networks that are supported by and, at the same time, support the flow of electrons down the Earth's redox gradient maintained along the core-mantle-crust-ocean-atmosphere axis of the planet. The presented findings lead to a radically new perspective on the nature and origin of life, suggesting that living matter is an organizational state/phase of nonliving matter and a natural consequence of the evolution and self-organization of nonliving matter.The presented paradigm opens doors for explosive advances in many disciplines, by uniting them within a single conceptual framework and providing a discovery method that allows for the systematic generation of knowledge through comparison and complementation of empirical data across different sciences and disciplines.

The self-organizing fractal theory as a universal discovery method: the phenomenon of life

PubMed Central

2011-01-01

A universal discovery method potentially applicable to all disciplines studying organizational phenomena has been developed. This method takes advantage of a new form of global symmetry, namely, scale-invariance of self-organizational dynamics of energy/matter at all levels of organizational hierarchy, from elementary particles through cells and organisms to the Universe as a whole. The method is based on an alternative conceptualization of physical reality postulating that the energy/matter comprising the Universe is far from equilibrium, that it exists as a flow, and that it develops via self-organization in accordance with the empirical laws of nonequilibrium thermodynamics. It is postulated that the energy/matter flowing through and comprising the Universe evolves as a multiscale, self-similar structure-process, i.e., as a self-organizing fractal. This means that certain organizational structures and processes are scale-invariant and are reproduced at all levels of the organizational hierarchy. Being a form of symmetry, scale-invariance naturally lends itself to a new discovery method that allows for the deduction of missing information by comparing scale-invariant organizational patterns across different levels of the organizational hierarchy. An application of the new discovery method to life sciences reveals that moving electrons represent a keystone physical force (flux) that powers, animates, informs, and binds all living structures-processes into a planetary-wide, multiscale system of electron flow/circulation, and that all living organisms and their larger-scale organizations emerge to function as electron transport networks that are supported by and, at the same time, support the flow of electrons down the Earth's redox gradient maintained along the core-mantle-crust-ocean-atmosphere axis of the planet. The presented findings lead to a radically new perspective on the nature and origin of life, suggesting that living matter is an organizational state/phase of nonliving matter and a natural consequence of the evolution and self-organization of nonliving matter. The presented paradigm opens doors for explosive advances in many disciplines, by uniting them within a single conceptual framework and providing a discovery method that allows for the systematic generation of knowledge through comparison and complementation of empirical data across different sciences and disciplines. PMID:21447162

Psychiatric Genomics: An Update and an Agenda.

PubMed

Sullivan, Patrick F; Agrawal, Arpana; Bulik, Cynthia M; Andreassen, Ole A; Børglum, Anders D; Breen, Gerome; Cichon, Sven; Edenberg, Howard J; Faraone, Stephen V; Gelernter, Joel; Mathews, Carol A; Nievergelt, Caroline M; Smoller, Jordan W; O'Donovan, Michael C

2018-01-01

The Psychiatric Genomics Consortium (PGC) is the largest consortium in the history of psychiatry. This global effort is dedicated to rapid progress and open science, and in the past decade it has delivered an increasing flow of new knowledge about the fundamental basis of common psychiatric disorders. The PGC has recently commenced a program of research designed to deliver "actionable" findings-genomic results that 1) reveal fundamental biology, 2) inform clinical practice, and 3) deliver new therapeutic targets. The central idea of the PGC is to convert the family history risk factor into biologically, clinically, and therapeutically meaningful insights. The emerging findings suggest that we are entering a phase of accelerated genetic discovery for multiple psychiatric disorders. These findings are likely to elucidate the genetic portions of these truly complex traits, and this knowledge can then be mined for its relevance for improved therapeutics and its impact on psychiatric practice within a precision medicine framework. [AJP at 175: Remembering Our Past As We Envision Our Future November 1946: The Genetic Theory of Schizophrenia Franz Kallmann's influential twin study of schizophrenia in 691 twin pairs was the largest in the field for nearly four decades. (Am J Psychiatry 1946; 103:309-322 )].

A framework of knowledge creation processes in participatory simulation of hospital work systems.

PubMed

Andersen, Simone Nyholm; Broberg, Ole

2017-04-01

Participatory simulation (PS) is a method to involve workers in simulating and designing their own future work system. Existing PS studies have focused on analysing the outcome, and minimal attention has been devoted to the process of creating this outcome. In order to study this process, we suggest applying a knowledge creation perspective. The aim of this study was to develop a framework describing the process of how ergonomics knowledge is created in PS. Video recordings from three projects applying PS of hospital work systems constituted the foundation of process mining analysis. The analysis resulted in a framework revealing the sources of ergonomics knowledge creation as sequential relationships between the activities of simulation participants sharing work experiences; experimenting with scenarios; and reflecting on ergonomics consequences. We argue that this framework reveals the hidden steps of PS that are essential when planning and facilitating PS that aims at designing work systems. Practitioner Summary: When facilitating participatory simulation (PS) in work system design, achieving an understanding of the PS process is essential. By applying a knowledge creation perspective and process mining, we investigated the knowledge-creating activities constituting the PS process. The analysis resulted in a framework of the knowledge-creating process in PS.

NASA/DoD Aerospace Knowledge Diffusion Research Project. XXIII - Information technology and aerospace knowledge diffusion: Exploring the intermediary-end user interface in a policy framework

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Barclay, Rebecca O.; Bishop, Ann P.; Kennedy, John M.

1992-01-01

Federal attempts to stimulate technological innovation have been unsuccessful because of the application of an inappropriate policy framework that lacks conceptual and empirical knowledge of the process of technological innovation and fails to acknowledge the relationship between knowledge production, transfer, and use as equally important components of the process of knowledge diffusion. This article argues that the potential contributions of high-speed computing and networking systems will be diminished unless empirically derived knowledge about the information-seeking behavior of members of the social system is incorporated into a new policy framework. Findings from the NASA/DoD Aerospace Knowledge Diffusion Research Project are presented in support of this assertion.

ASIS 2000: Knowledge Innovations: Celebrating Our Heritage, Designing Our Future. Proceedings of the ASIS Annual Meeting (63rd, Chicago, Illinois, November 12-16, 2000). Volume 37.

ERIC Educational Resources Information Center

Kraft, Donald H., Ed.

The 2000 ASIS (American Society for Information Science) conference explored knowledge innovation. The tracks in the conference program included knowledge discovery, capture, and creation; classification and representation; information retrieval; knowledge dissemination; and social, behavioral, ethical, and legal aspects. This proceedings is…

IMG-ABC: A Knowledge Base To Fuel Discovery of Biosynthetic Gene Clusters and Novel Secondary Metabolites.

PubMed

Hadjithomas, Michalis; Chen, I-Min Amy; Chu, Ken; Ratner, Anna; Palaniappan, Krishna; Szeto, Ernest; Huang, Jinghua; Reddy, T B K; Cimermančič, Peter; Fischbach, Michael A; Ivanova, Natalia N; Markowitz, Victor M; Kyrpides, Nikos C; Pati, Amrita

2015-07-14

In the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of "big" genomic data for discovering small molecules. IMG-ABC relies on IMG's comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC's focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time in Alphaproteobacteria. IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules. IMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG's extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world. Copyright © 2015 Hadjithomas et al.

Brokerage services for Earth Science data: the EuroGEOSS legacy (Invited)

NASA Astrophysics Data System (ADS)

Nativi, S.; Craglia, M.; Pearlman, J.

2013-12-01

Global sustainability research requires an integrated multidisciplinary effort underpinned by a collaborative environment discovering and accessing heterogeneous data across disciplines. Traditionally, interoperability has been achieved by implementing federation of systems. The federating approach entails the adoption of a set of common technologies and standards. This presentation argues that for complex (and uncontrolled) environments (such as global, multidisciplinary, and voluntary-based infrastructures) federated solutions must be completed and enhanced by a brokering approach -making available a set of brokerage services. In fact, brokerage services allows a cyber-infrastructure to lower entry barriers (for both data producers and users) and to better address the different domain specificities. The brokering interoperability approach was successfully experimented by the EuroGEOSS project, funded by the European Commission in the FP7 framework (see http://www.eurogeoss.eu). The EuroGEOSS Brokering framework provided the EuroGEOSS Capacity with multidisciplinary interoperability functionalities. This platform was developed applying several of the principles/requirements that characterize the System of Systems (SoS) approach and the Internet of Services (IoS) philosophy. The framework consists of three main brokers (middleware components implementing intermediation and harmonization services): a basic Discovery Broker, an advanced Semantic Discovery Broker, and an Access Broker. They are empowered by a suite of tools developed by the ESSI-lab of the CNR-IIA, called: GI-cat, GI-sem, and GI-axe. The EuroGEOSS brokering framework was considered and successfully adopted by cross-disciplinary initiatives (notably GEOSS: Global Earth Observation System of Systems). The brokerage services have been advanced and extended; the new brokering framework is called GEO DAB (Discovery and Access Broker). New brokerage services have been developed in the framework of other European Commission funded projects (e.g. GeoViQua). More recently, the NSF EarthCube initiative decided to fund a project dealing with brokerage services. In the framework of the GEO AIP-6 (Architecture Implementation Pilot -phase 6), the presented brokerage platform has been used by the Water Working Group to carry out improved data access for parameterization and model development.

Evaluating the Science of Discovery in Complex Health Systems

ERIC Educational Resources Information Center

Norman, Cameron D.; Best, Allan; Mortimer, Sharon; Huerta, Timothy; Buchan, Alison

2011-01-01

Complex health problems such as chronic disease or pandemics require knowledge that transcends disciplinary boundaries to generate solutions. Such transdisciplinary discovery requires researchers to work and collaborate across boundaries, combining elements of basic and applied science. At the same time, calls for more interdisciplinary health…

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-07-01

... administrative law judge in accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the... things and the identity and location of persons having knowledge of any discoverable matter. (b) It is...

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

... accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature... location of persons having knowledge of any discoverable matter. (b) It is not ground for objection that...

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

... accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature... location of persons having knowledge of any discoverable matter. (b) It is not ground for objection that...

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-07-01

... administrative law judge in accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the... things and the identity and location of persons having knowledge of any discoverable matter. (b) It is...

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

... accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature... location of persons having knowledge of any discoverable matter. (b) It is not ground for objection that...

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-07-01

... administrative law judge in accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the... things and the identity and location of persons having knowledge of any discoverable matter. (b) It is...

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-07-01

... administrative law judge in accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the... things and the identity and location of persons having knowledge of any discoverable matter. (b) It is...

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

... accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature... location of persons having knowledge of any discoverable matter. (b) It is not ground for objection that...

Key principles for a national clinical decision support knowledge sharing framework: synthesis of insights from leading subject matter experts

PubMed Central

Hongsermeier, Tonya; Wright, Adam; Lewis, Janet; Bell, Douglas S; Middleton, Blackford

2013-01-01

Objective To identify key principles for establishing a national clinical decision support (CDS) knowledge sharing framework. Materials and methods As part of an initiative by the US Office of the National Coordinator for Health IT (ONC) to establish a framework for national CDS knowledge sharing, key stakeholders were identified. Stakeholders' viewpoints were obtained through surveys and in-depth interviews, and findings and relevant insights were summarized. Based on these insights, key principles were formulated for establishing a national CDS knowledge sharing framework. Results Nineteen key stakeholders were recruited, including six executives from electronic health record system vendors, seven executives from knowledge content producers, three executives from healthcare provider organizations, and three additional experts in clinical informatics. Based on these stakeholders' insights, five key principles were identified for effectively sharing CDS knowledge nationally. These principles are (1) prioritize and support the creation and maintenance of a national CDS knowledge sharing framework; (2) facilitate the development of high-value content and tooling, preferably in an open-source manner; (3) accelerate the development or licensing of required, pragmatic standards; (4) acknowledge and address medicolegal liability concerns; and (5) establish a self-sustaining business model. Discussion Based on the principles identified, a roadmap for national CDS knowledge sharing was developed through the ONC's Advancing CDS initiative. Conclusion The study findings may serve as a useful guide for ongoing activities by the ONC and others to establish a national framework for sharing CDS knowledge and improving clinical care. PMID:22865671

Trends in Modern Drug Discovery.

PubMed

Eder, Jörg; Herrling, Paul L

2016-01-01

Drugs discovered by the pharmaceutical industry over the past 100 years have dramatically changed the practice of medicine and impacted on many aspects of our culture. For many years, drug discovery was a target- and mechanism-agnostic approach that was based on ethnobotanical knowledge often fueled by serendipity. With the advent of modern molecular biology methods and based on knowledge of the human genome, drug discovery has now largely changed into a hypothesis-driven target-based approach, a development which was paralleled by significant environmental changes in the pharmaceutical industry. Laboratories became increasingly computerized and automated, and geographically dispersed research sites are now more and more clustered into large centers to capture technological and biological synergies. Today, academia, the regulatory agencies, and the pharmaceutical industry all contribute to drug discovery, and, in order to translate the basic science into new medical treatments for unmet medical needs, pharmaceutical companies have to have a critical mass of excellent scientists working in many therapeutic fields, disciplines, and technologies. The imperative for the pharmaceutical industry to discover breakthrough medicines is matched by the increasing numbers of first-in-class drugs approved in recent years and reflects the impact of modern drug discovery approaches, technologies, and genomics.

Using ICT to Enhance Knowledge Management in Higher Education: A Conceptual Framework and Research Agenda

ERIC Educational Resources Information Center

Omona, Walter; van der Weide, Theo; Lubega, Jude

2010-01-01

The adoption and use of ICT to enhance and facilitate Knowledge Management (KM) has brought to focus the urgent need to come out with new methods, tools and techniques in the development of KM systems frameworks, knowledge processes and knowledge technologies to promote effective management of knowledge for improved service deliveries in higher…

Narrative review of frameworks for translating research evidence into policy and practice.

PubMed

Milat, Andrew J; Li, Ben

2017-02-15

A significant challenge in research translation is that interested parties interpret and apply the associated terms and conceptual frameworks in different ways. The purpose of this review was to: a) examine different research translation frameworks; b) examine the similarities and differences between the frameworks; and c) identify key strengths and weaknesses of the models when they are applied in practice. The review involved a keyword search of PubMed. The search string was (translational research OR knowledge translation OR evidence to practice) AND (framework OR model OR theory) AND (public health OR health promotion OR medicine). Included studies were published in English between January 1990 and December 2014, and described frameworks, models or theories associated with research translation. The final review included 98 papers, and 41 different frameworks and models were identified. The most frequently applied knowledge translation framework in the literature was RE-AIM, followed by the knowledge translation continuum or 'T' models, the Knowledge to Action framework, the PARiHS framework, evidence based public health models, and the stages of research and evaluation model. The models identified in this review stem from different fields, including implementation science, basic and medical sciences, health services research and public health, and propose different but related pathways to closing the research-practice gap.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Defining Malaysian Knowledge Society: Results from the Delphi Technique

NASA Astrophysics Data System (ADS)

Hamid, Norsiah Abdul; Zaman, Halimah Badioze

This paper outlines the findings of research where the central idea is to define the term Knowledge Society (KS) in Malaysian context. The research focuses on three important dimensions, namely knowledge, ICT and human capital. This study adopts a modified Delphi technique to seek the important dimensions that can contribute to the development of Malaysian's KS. The Delphi technique involved ten experts in a five-round iterative and controlled feedback procedure to obtain consensus on the important dimensions and to verify the proposed definition of KS. The finding shows that all three dimensions proposed initially scored high and moderate consensus. Round One (R1) proposed an initial definition of KS and required comments and inputs from the panel. These inputs were then used to develop items for a R2 questionnaire. In R2, 56 out of 73 items scored high consensus and in R3, 63 out of 90 items scored high. R4 was conducted to re-rate the new items, in which 8 out of 17 items scored high. Other items scored moderate consensus and no item scored low or no consensus in all rounds. The final round (R5) was employed to verify the final definition of KS. Findings and discovery of this study are significant to the definition of KS and the development of a framework in the Malaysian context.

Knowledge for better health: a conceptual framework and foundation for health research systems.

PubMed Central

Pang, Tikki; Sadana, Ritu; Hanney, Steve; Bhutta, Zulfiqar A.; Hyder, Adnan A.; Simon, Jonathon

2003-01-01

Health research generates knowledge that can be utilized to improve health system performance and, ultimately, health and health equity. We propose a conceptual framework for health research systems (HRSs) that defines their boundaries, components, goals, and functions. The framework adopts a systems perspective towards HRSs and serves as a foundation for constructing a practical approach to describe and analyse HRSs. The analysis of HRSs should, in turn, provide a better understanding of how research contributes to gains in health and health equity. In this framework, the intrinsic goals of the HRS are the advancement of scientific knowledge and the utilization of knowledge to improve health and health equity. Its four principal functions are stewardship, financing, creating and sustaining resources, and producing and using research. The framework, as it is applied in consultation with countries, will provide countries and donor agencies with relevant inputs to policies and strategies for strengthening HRSs and using knowledge for better health. PMID:14758408

Knowledge for better health: a conceptual framework and foundation for health research systems.

PubMed

Pang, Tikki; Sadana, Ritu; Hanney, Steve; Bhutta, Zulfiqar A; Hyder, Adnan A; Simon, Jonathon

2003-01-01

Health research generates knowledge that can be utilized to improve health system performance and, ultimately, health and health equity. We propose a conceptual framework for health research systems (HRSs) that defines their boundaries, components, goals, and functions. The framework adopts a systems perspective towards HRSs and serves as a foundation for constructing a practical approach to describe and analyse HRSs. The analysis of HRSs should, in turn, provide a better understanding of how research contributes to gains in health and health equity. In this framework, the intrinsic goals of the HRS are the advancement of scientific knowledge and the utilization of knowledge to improve health and health equity. Its four principal functions are stewardship, financing, creating and sustaining resources, and producing and using research. The framework, as it is applied in consultation with countries, will provide countries and donor agencies with relevant inputs to policies and strategies for strengthening HRSs and using knowledge for better health.

ENTEL: A Case Study on Knowledge Networks and the Impact of Web 2.0 Technologies

ERIC Educational Resources Information Center

Griffiths, Paul; Arenas, Teresita

2014-01-01

This study re-visits an organisation that defined its knowledge-management strategy in 2008-9 applying an established strategy-intellectual capital alignment framework. It addresses questions "How has knowledge management evolved at ENTEL, and what lessons can be learnt? Does the strategy-knowledge management alignment framework applied at…

Study of Sharing Knowledge Resources in Business Schools

ERIC Educational Resources Information Center

Ranjan, Jayanthi

2011-01-01

Purpose: The purpose of this paper is to propose a common business school framework based on knowledge resources that are available in business schools. To support the arguments made based on review literature, the paper presents the holistic framework of knowledge resources in a business school and also provides a knowledge value chain in sharing…

Reuniting Virtue and Knowledge

ERIC Educational Resources Information Center

Culham, Tom

2015-01-01

Einstein held that intuition is more important than rational inquiry as a source of discovery. Further, he explicitly and implicitly linked the heart, the sacred, devotion and intuitive knowledge. The raison d'être of universities is the advance of knowledge; however, they have primarily focused on developing student's skills in working with…

Two frameworks for integrating knowledge in induction

NASA Technical Reports Server (NTRS)

Rosenbloom, Paul S.; Hirsh, Haym; Cohen, William W.; Smith, Benjamin D.

1994-01-01

The use of knowledge in inductive learning is critical for improving the quality of the concept definitions generated, reducing the number of examples required in order to learn effective concept definitions, and reducing the computation needed to find good concept definitions. Relevant knowledge may come in many forms (such as examples, descriptions, advice, and constraints) and from many sources (such as books, teachers, databases, and scientific instruments). How to extract the relevant knowledge from this plethora of possibilities, and then to integrate it together so as to appropriately affect the induction process is perhaps the key issue at this point in inductive learning. Here the focus is on the integration part of this problem; that is, how induction algorithms can, and do, utilize a range of extracted knowledge. Preliminary work on a transformational framework for defining knowledge-intensive inductive algorithms out of relatively knowledge-free algorithms is described, as is a more tentative problems-space framework that attempts to cover all induction algorithms within a single general approach. These frameworks help to organize what is known about current knowledge-intensive induction algorithms, and to point towards new algorithms.

Integrative Systems Biology for Data Driven Knowledge Discovery

PubMed Central

Greene, Casey S.; Troyanskaya, Olga G.

2015-01-01

Integrative systems biology is an approach that brings together diverse high throughput experiments and databases to gain new insights into biological processes or systems at molecular through physiological levels. These approaches rely on diverse high-throughput experimental techniques that generate heterogeneous data by assaying varying aspects of complex biological processes. Computational approaches are necessary to provide an integrative view of these experimental results and enable data-driven knowledge discovery. Hypotheses generated from these approaches can direct definitive molecular experiments in a cost effective manner. Using integrative systems biology approaches, we can leverage existing biological knowledge and large-scale data to improve our understanding of yet unknown components of a system of interest and how its malfunction leads to disease. PMID:21044756

Automated Knowledge Discovery from Simulators

NASA Technical Reports Server (NTRS)

Burl, Michael C.; DeCoste, D.; Enke, B. L.; Mazzoni, D.; Merline, W. J.; Scharenbroich, L.

2006-01-01

In this paper, we explore one aspect of knowledge discovery from simulators, the landscape characterization problem, where the aim is to identify regions in the input/ parameter/model space that lead to a particular output behavior. Large-scale numerical simulators are in widespread use by scientists and engineers across a range of government agencies, academia, and industry; in many cases, simulators provide the only means to examine processes that are infeasible or impossible to study otherwise. However, the cost of simulation studies can be quite high, both in terms of the time and computational resources required to conduct the trials and the manpower needed to sift through the resulting output. Thus, there is strong motivation to develop automated methods that enable more efficient knowledge extraction.

18 CFR 385.402 - Scope of discovery (Rule 402).

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Scope of discovery (Rule 402). 385.402 Section 385.402 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... persons having any knowledge of any discoverable matter. It is not ground for objection that the...

Doors to Discovery[TM]. What Works Clearinghouse Intervention Report

ERIC Educational Resources Information Center

What Works Clearinghouse, 2013

2013-01-01

"Doors to Discovery"]TM] is a preschool literacy curriculum that uses eight thematic units of activities to help children build fundamental early literacy skills in oral language, phonological awareness, concepts of print, alphabet knowledge, writing, and comprehension. The eight thematic units cover topics such as nature, friendship,…

78 FR 12933 - Proceedings Before the Commodity Futures Trading Commission

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-26

... proceedings. These new amendments also provide that Judgment Officers may conduct sua sponte discovery in... discovery; (4) sound risk management practices; and (5) other public interest considerations. The amendments... representative capacity, it was done with full power and authority to do so; (C) To the best of his knowledge...

76 FR 64803 - Rules of Adjudication and Enforcement

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-19

...) is also amended to clarify the limits on discovery when the Commission orders the ALJ to consider the... that the complainant identify, to the best of its knowledge, the ``like or directly competitive... the taking of discovery by the parties shall be at the discretion of the presiding ALJ. The ITCTLA...

78 FR 63253 - Davidson Kempner Capital Management LLC; Notice of Application

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

... employees of the Adviser other than the Contributor have any knowledge of the Contribution prior to its discovery by the Adviser on November 2, 2011. The Contribution was discovered by the Adviser's compliance... names of employees. After discovery of the Contribution, the Adviser and Contributor obtained the...

Outcomes-focused knowledge translation: a framework for knowledge translation and patient outcomes improvement.

PubMed

Doran, Diane M; Sidani, Souraya

2007-01-01

Regularly accessing information that is current and reliable continues to be a challenge for front-line staff nurses. Reconceptualizing how nurses access information and designing appropriate decision support systems to facilitate timely access to information may be important for increasing research utilization. An outcomes-focused knowledge translation framework was developed to guide the continuous improvement of patient care through the uptake of research evidence and feedback data about patient outcomes. The framework operationalizes the three elements of the PARIHS framework at the point of care. Outcomes-focused knowledge translation involves four components: (a) patient outcomes measurement and real-time feedback about outcomes achievement; (b) best-practice guidelines, embedded in decision support tools that deliver key messages in response to patient assessment data; (c) clarification of patients' preferences for care; and (d) facilitation by advanced practice nurses and practice leaders. In this paper the framework is described and evidence is provided to support theorized relationships among the concepts in the framework. The framework guided the design of a knowledge translation intervention aimed at continuous improvement of patient care and evidence-based practice, which are fostered through real-time feedback data about patient outcomes, electronic access to evidence-based resources at the point of care, and facilitation by advanced practice nurses. The propositions in the framework need to be empirically tested through future research.

Does the knowledge-to-action (KTA) framework facilitate physical demands analysis development for firefighter injury management and return-to-work planning?

PubMed

Sinden, Kathryn; MacDermid, Joy C

2014-03-01

Employers are tasked with developing injury management and return-to-work (RTW) programs in response to occupational health and safety policies. Physical demands analyses (PDAs) are the cornerstone of injury management and RTW development. Synthesizing and contextualizing policy knowledge for use in occupational program development, including PDAs, is challenging due to multiple stakeholder involvement. Few studies have used a knowledge translation theoretical framework to facilitate policy-based interventions in occupational contexts. The primary aim of this case study was to identify how constructs of the knowledge-to-action (KTA) framework were reflected in employer stakeholder-researcher collaborations during development of a firefighter PDA. Four stakeholder meetings were conducted with employee participants who had experience using PDAs in their occupational role. Directed content analysis informed analyses of meeting minutes, stakeholder views and personal reflections recorded throughout the case. Existing knowledge sources including local data, stakeholder experiences, policies and priorities were synthesized and tailored to develop a PDA in response to the barriers and facilitators identified by the firefighters. The flexibility of the KTA framework and synthesis of multiple knowledge sources were identified strengths. The KTA Action cycle was useful in directing the overall process but insufficient for directing the specific aspects of PDA development. Integration of specific PDA guidelines into the process provided explicit direction on best practices in tailoring the PDA and knowledge synthesis. Although the themes of the KTA framework were confirmed in our analysis, order modification of the KTA components was required. Despite a complex context with divergent perspectives successful implementation of a draft PDA was achieved. The KTA framework facilitated knowledge synthesis and PDA development but specific standards and modifications to the KTA framework were needed to enhance process structure. Flexibility for modification and integration of PDA practice guidelines were identified as assets of the KTA framework during its application.

Automated Discovery of Simulation Between Programs

DTIC Science & Technology

2014-10-18

relation. These relations enable the refinement-step of SimAbs. We have implemented SimAbs using UFO framework and Z3 SMT-solver and applied it to...step of SimAbs. We implemented SimAbs and AE-VAL on the top of the UFO framework [1, 15] and an SMT-solver Z3 [8], respectively. We have evaluated SimAbs...ut 6 Evaluation We have implemented SimAbs in the UFO framework, and evaluated it on the Software Verification Competition (SVCOMP’14) benchmarks and

Network-based approaches to climate knowledge discovery

NASA Astrophysics Data System (ADS)

Budich, Reinhard; Nyberg, Per; Weigel, Tobias

2011-11-01

Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.

A Framework for Seamless Interoperation of Heterogeneous Distributed Software Components

DTIC Science & Technology

2005-05-01

interoperability, b) distributed resource discovery, and c) validation of quality requirements. Principles and prototypical systems were created to demonstrate the successful completion of the research.

Bridging the Gap in Neurotherapeutic Discovery and Development: The Role of the National Institute of Neurological Disorders and Stroke in Translational Neuroscience.

PubMed

Mott, Meghan; Koroshetz, Walter

2015-07-01

The mission of the National Institute of Neurological Disorders and Stroke (NINDS) is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease. NINDS supports early- and late-stage therapy development funding programs to accelerate preclinical discovery and the development of new therapeutic interventions for neurological disorders. The NINDS Office of Translational Research facilitates and funds the movement of discoveries from the laboratory to patients. Its grantees include academics, often with partnerships with the private sector, as well as small businesses, which, by Congressional mandate, receive > 3% of the NINDS budget for small business innovation research. This article provides an overview of NINDS-funded therapy development programs offered by the NINDS Office of Translational Research.

Generic-distributed framework for cloud services marketplace based on unified ontology.

PubMed

Hasan, Samer; Valli Kumari, V

2017-11-01

Cloud computing is a pattern for delivering ubiquitous and on demand computing resources based on pay-as-you-use financial model. Typically, cloud providers advertise cloud service descriptions in various formats on the Internet. On the other hand, cloud consumers use available search engines (Google and Yahoo) to explore cloud service descriptions and find the adequate service. Unfortunately, general purpose search engines are not designed to provide a small and complete set of results, which makes the process a big challenge. This paper presents a generic-distrusted framework for cloud services marketplace to automate cloud services discovery and selection process, and remove the barriers between service providers and consumers. Additionally, this work implements two instances of generic framework by adopting two different matching algorithms; namely dominant and recessive attributes algorithm borrowed from gene science and semantic similarity algorithm based on unified cloud service ontology. Finally, this paper presents unified cloud services ontology and models the real-life cloud services according to the proposed ontology. To the best of the authors' knowledge, this is the first attempt to build a cloud services marketplace where cloud providers and cloud consumers can trend cloud services as utilities. In comparison with existing work, semantic approach reduced the execution time by 20% and maintained the same values for all other parameters. On the other hand, dominant and recessive attributes approach reduced the execution time by 57% but showed lower value for recall.

Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

PubMed Central

Frijters, Raoul; van Vugt, Marianne; Smeets, Ruben; van Schaik, René; de Vlieg, Jacob; Alkema, Wynand

2010-01-01

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs. PMID:20885778

Interdisciplinary Laboratory Course Facilitating Knowledge Integration, Mutualistic Teaming, and Original Discovery.

PubMed

Full, Robert J; Dudley, Robert; Koehl, M A R; Libby, Thomas; Schwab, Cheryl

2015-11-01

Experiencing the thrill of an original scientific discovery can be transformative to students unsure about becoming a scientist, yet few courses offer authentic research experiences. Increasingly, cutting-edge discoveries require an interdisciplinary approach not offered in current departmental-based courses. Here, we describe a one-semester, learning laboratory course on organismal biomechanics offered at our large research university that enables interdisciplinary teams of students from biology and engineering to grow intellectually, collaborate effectively, and make original discoveries. To attain this goal, we avoid traditional "cookbook" laboratories by training 20 students to use a dozen research stations. Teams of five students rotate to a new station each week where a professor, graduate student, and/or team member assists in the use of equipment, guides students through stages of critical thinking, encourages interdisciplinary collaboration, and moves them toward authentic discovery. Weekly discussion sections that involve the entire class offer exchange of discipline-specific knowledge, advice on experimental design, methods of collecting and analyzing data, a statistics primer, and best practices for writing and presenting scientific papers. The building of skills in concert with weekly guided inquiry facilitates original discovery via a final research project that can be presented at a national meeting or published in a scientific journal. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

The knowledge-value chain: A conceptual framework for knowledge translation in health.

PubMed

Landry, Réjean; Amara, Nabil; Pablos-Mendes, Ariel; Shademani, Ramesh; Gold, Irving

2006-08-01

This article briefly discusses knowledge translation and lists the problems associated with it. Then it uses knowledge-management literature to develop and propose a knowledge-value chain framework in order to provide an integrated conceptual model of knowledge management and application in public health organizations. The knowledge-value chain is a non-linear concept and is based on the management of five dyadic capabilities: mapping and acquisition, creation and destruction, integration and sharing/transfer, replication and protection, and performance and innovation.

The knowledge-value chain: A conceptual framework for knowledge translation in health.

PubMed Central

Landry, Réjean; Amara, Nabil; Pablos-Mendes, Ariel; Shademani, Ramesh; Gold, Irving

2006-01-01

This article briefly discusses knowledge translation and lists the problems associated with it. Then it uses knowledge-management literature to develop and propose a knowledge-value chain framework in order to provide an integrated conceptual model of knowledge management and application in public health organizations. The knowledge-value chain is a non-linear concept and is based on the management of five dyadic capabilities: mapping and acquisition, creation and destruction, integration and sharing/transfer, replication and protection, and performance and innovation. PMID:16917645

6-D, A Process Framework for the Design and Development of Web-based Systems.

ERIC Educational Resources Information Center

Christian, Phillip

2001-01-01

Explores how the 6-D framework can form the core of a comprehensive systemic strategy and help provide a supporting structure for more robust design and development while allowing organizations to support whatever methods and models best suit their purpose. 6-D stands for the phases of Web design and development: Discovery, Definition, Design,…

Knowledge translation is the use of knowledge in health care decision making.

PubMed

Straus, Sharon E; Tetroe, Jacqueline M; Graham, Ian D

2011-01-01

To provide an overview of the science and practice of knowledge translation. Narrative review outlining what knowledge translation is and a framework for its use. Knowledge translation is defined as the use of knowledge in practice and decision making by the public, patients, health care professionals, managers, and policy makers. Failures to use research evidence to inform decision making are apparent across all these key decision maker groups. There are several proposed theories and frameworks for achieving knowledge translation. A conceptual framework developed by Graham et al., termed the knowledge-to-action cycle, provides an approach that builds on the commonalities found in an assessment of planned action theories. Review of the evidence base for the science and practice of knowledge translation has identified several gaps including the need to develop valid strategies for assessing the determinants of knowledge use and for evaluating sustainability of knowledge translation interventions. Copyright © 2011 Elsevier Inc. All rights reserved.

Strengthening TPACK: A Broader Notion of Context and the Use of Teacher's Narratives to Reveal Knowledge Construction

ERIC Educational Resources Information Center

Porras-Hernandez, Laura Helena; Salinas-Amescua, Bertha

2013-01-01

Technological Pedagogical Content Knowledge (TPACK) as a framework to understand and foster teachers' knowledge for efficient technology integration has the value of unveiling new types of knowledge and departing from technocentric approaches. In this article, we consider two approaches to advance this framework. One of these opens the discussion…

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 23: Information technology and aerospace knowledge diffusion: Exploring the intermediary-end user interface in a policy framework

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Barclay, Rebecca O.; Bishop, Ann P.; Kennedy, John M.

1992-01-01

Federal attempts to stimulate technological innovation have been unsuccessful because of the application of an inappropriate policy framework that lacks conceptual and empirical knowledge of the process of technological innovation and fails to acknowledge the relationship between knowled reproduction, transfer, and use as equally important components of the process of knowledge diffusion. It is argued that the potential contributions of high-speed computing and networking systems will be diminished unless empirically derived knowledge about the information-seeking behavior of the members of the social system is incorporated into a new policy framework. Findings from the NASA/DoD Aerospace Knowledge Diffusion Research Project are presented in support of this assertion.

Asymmetric threat data mining and knowledge discovery

NASA Astrophysics Data System (ADS)

Gilmore, John F.; Pagels, Michael A.; Palk, Justin

2001-03-01

Asymmetric threats differ from the conventional force-on- force military encounters that the Defense Department has historically been trained to engage. Terrorism by its nature is now an operational activity that is neither easily detected or countered as its very existence depends on small covert attacks exploiting the element of surprise. But terrorism does have defined forms, motivations, tactics and organizational structure. Exploiting a terrorism taxonomy provides the opportunity to discover and assess knowledge of terrorist operations. This paper describes the Asymmetric Threat Terrorist Assessment, Countering, and Knowledge (ATTACK) system. ATTACK has been developed to (a) data mine open source intelligence (OSINT) information from web-based newspaper sources, video news web casts, and actual terrorist web sites, (b) evaluate this information against a terrorism taxonomy, (c) exploit country/region specific social, economic, political, and religious knowledge, and (d) discover and predict potential terrorist activities and association links. Details of the asymmetric threat structure and the ATTACK system architecture are presented with results of an actual terrorist data mining and knowledge discovery test case shown.

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning.

PubMed

Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Rebholz-Schuhmann, Dietrich; Schofield, Paul N; Gkoutos, Georgios V

2011-01-01

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.

Conceptual dissonance: evaluating the efficacy of natural language processing techniques for validating translational knowledge constructs.

PubMed

Payne, Philip R O; Kwok, Alan; Dhaval, Rakesh; Borlawsky, Tara B

2009-03-01

The conduct of large-scale translational studies presents significant challenges related to the storage, management and analysis of integrative data sets. Ideally, the application of methodologies such as conceptual knowledge discovery in databases (CKDD) provides a means for moving beyond intuitive hypothesis discovery and testing in such data sets, and towards the high-throughput generation and evaluation of knowledge-anchored relationships between complex bio-molecular and phenotypic variables. However, the induction of such high-throughput hypotheses is non-trivial, and requires correspondingly high-throughput validation methodologies. In this manuscript, we describe an evaluation of the efficacy of a natural language processing-based approach to validating such hypotheses. As part of this evaluation, we will examine a phenomenon that we have labeled as "Conceptual Dissonance" in which conceptual knowledge derived from two or more sources of comparable scope and granularity cannot be readily integrated or compared using conventional methods and automated tools.

A Hyperknowledge Framework of Decision Support Systems.

ERIC Educational Resources Information Center

Chang, Ai-Mei; And Others

1994-01-01

Presents a hyperknowledge framework of decision support systems (DSS). This framework formalizes specifics about system functionality, representation of knowledge, navigation of the knowledge system, and user-interface traits as elements of a DSS environment that conforms closely to human cognitive processes in decision making. (Contains 52…

A Framework for Teaching Tactical Game Knowledge.

ERIC Educational Resources Information Center

Wilson, Gail E.

2002-01-01

Provides an example of a framework of generic knowledge, designed for teachers, that describes and explains the foundational tactical aspects of invasive team-game play. The framework consists of four modules: participants and their roles, objectives, action principles, and action options. Guidelines to help instructors design practical activities…

Confirming the Stankosky Knowledge Management Framework

ERIC Educational Resources Information Center

Ternes, Carl D., Jr.

2011-01-01

As a managerial construct, knowledge management (KM) optimizes organizational knowledge assets to achieve sustainable business advantages by connecting people with the intellectual resources needed to operate more effectively. Yet KM may have its greatest impact when used with repeatable, systems engineering-based "frameworks." As such, this study…

Knowledge discovery from data and Monte-Carlo DEA to evaluate technical efficiency of mental health care in small health areas

PubMed Central

García-Alonso, Carlos; Pérez-Naranjo, Leonor

2009-01-01

Introduction Knowledge management, based on information transfer between experts and analysts, is crucial for the validity and usability of data envelopment analysis (DEA). Aim To design and develop a methodology: i) to assess technical efficiency of small health areas (SHA) in an uncertainty environment, and ii) to transfer information between experts and operational models, in both directions, for improving expert’s knowledge. Method A procedure derived from knowledge discovery from data (KDD) is used to select, interpret and weigh DEA inputs and outputs. Based on KDD results, an expert-driven Monte-Carlo DEA model has been designed to assess the technical efficiency of SHA in Andalusia. Results In terms of probability, SHA 29 is the most efficient being, on the contrary, SHA 22 very inefficient. 73% of analysed SHA have a probability of being efficient (Pe) >0.9 and 18% <0.5. Conclusions Expert knowledge is necessary to design and validate any operational model. KDD techniques make the transfer of information from experts to any operational model easy and results obtained from the latter improve expert’s knowledge.

False Discovery Control in Large-Scale Spatial Multiple Testing

PubMed Central

Sun, Wenguang; Reich, Brian J.; Cai, T. Tony; Guindani, Michele; Schwartzman, Armin

2014-01-01

Summary This article develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both point-wise and cluster-wise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets. Numerical results show that the proposed procedures lead to more accurate error control and better power performance than conventional methods. We demonstrate our methods for analyzing the time trends in tropospheric ozone in eastern US. PMID:25642138

18 CFR 385.403 - Methods of discovery; general provisions (Rule 403).

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Methods of discovery; general provisions (Rule 403). 385.403 Section 385.403 Conservation of Power and Water Resources FEDERAL... the response is true and accurate to the best of that person's knowledge, information, and belief...

Evaluation Techniques for the Sandy Point Discovery Center, Great Bay National Estuarine Research Reserve.

ERIC Educational Resources Information Center

Heffernan, Bernadette M.

1998-01-01

Describes work done to provide staff of the Sandy Point Discovery Center with methods for evaluating exhibits and interpretive programming. Quantitative and qualitative evaluation measures were designed to assess the program's objective of estuary education. Pretest-posttest questionnaires and interviews are used to measure subjects' knowledge and…

The Prehistory of Discovery: Precursors of Representational Change in Solving Gear System Problems.

ERIC Educational Resources Information Center

Dixon, James A.; Bangert, Ashley S.

2002-01-01

This study investigated whether the process of representational change undergoes developmental change or different processes occupy different niches in the course of knowledge acquisition. Subjects--college, third-, and sixth-grade students--solved gear system problems over two sessions. Findings indicated that for all grades, discovery of the…

40 CFR 300.300 - Phase I-Discovery or notification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 27 2010-07-01 2010-07-01 false Phase I-Discovery or notification. 300.300 Section 300.300 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SUPERFUND... person in charge of a vessel or a facility shall, as soon as he or she has knowledge of any discharge...

Need to Knowledge (NtK) Model: an evidence-based framework for generating technological innovations with socio-economic impacts.

PubMed

Flagg, Jennifer L; Lane, Joseph P; Lockett, Michelle M

2013-02-15

Traditional government policies suggest that upstream investment in scientific research is necessary and sufficient to generate technological innovations. The expected downstream beneficial socio-economic impacts are presumed to occur through non-government market mechanisms. However, there is little quantitative evidence for such a direct and formulaic relationship between public investment at the input end and marketplace benefits at the impact end. Instead, the literature demonstrates that the technological innovation process involves a complex interaction between multiple sectors, methods, and stakeholders. The authors theorize that accomplishing the full process of technological innovation in a deliberate and systematic manner requires an operational-level model encompassing three underlying methods, each designed to generate knowledge outputs in different states: scientific research generates conceptual discoveries; engineering development generates prototype inventions; and industrial production generates commercial innovations. Given the critical roles of engineering and business, the entire innovation process should continuously consider the practical requirements and constraints of the commercial marketplace.The Need to Knowledge (NtK) Model encompasses the activities required to successfully generate innovations, along with associated strategies for effectively communicating knowledge outputs in all three states to the various stakeholders involved. It is intentionally grounded in evidence drawn from academic analysis to facilitate objective and quantitative scrutiny, and industry best practices to enable practical application. The Need to Knowledge (NtK) Model offers a practical, market-oriented approach that avoids the gaps, constraints and inefficiencies inherent in undirected activities and disconnected sectors. The NtK Model is a means to realizing increased returns on public investments in those science and technology programs expressly intended to generate beneficial socio-economic impacts.

Need to Knowledge (NtK) Model: an evidence-based framework for generating technological innovations with socio-economic impacts

PubMed Central

2013-01-01

Background Traditional government policies suggest that upstream investment in scientific research is necessary and sufficient to generate technological innovations. The expected downstream beneficial socio-economic impacts are presumed to occur through non-government market mechanisms. However, there is little quantitative evidence for such a direct and formulaic relationship between public investment at the input end and marketplace benefits at the impact end. Instead, the literature demonstrates that the technological innovation process involves a complex interaction between multiple sectors, methods, and stakeholders. Discussion The authors theorize that accomplishing the full process of technological innovation in a deliberate and systematic manner requires an operational-level model encompassing three underlying methods, each designed to generate knowledge outputs in different states: scientific research generates conceptual discoveries; engineering development generates prototype inventions; and industrial production generates commercial innovations. Given the critical roles of engineering and business, the entire innovation process should continuously consider the practical requirements and constraints of the commercial marketplace. The Need to Knowledge (NtK) Model encompasses the activities required to successfully generate innovations, along with associated strategies for effectively communicating knowledge outputs in all three states to the various stakeholders involved. It is intentionally grounded in evidence drawn from academic analysis to facilitate objective and quantitative scrutiny, and industry best practices to enable practical application. Summary The Need to Knowledge (NtK) Model offers a practical, market-oriented approach that avoids the gaps, constraints and inefficiencies inherent in undirected activities and disconnected sectors. The NtK Model is a means to realizing increased returns on public investments in those science and technology programs expressly intended to generate beneficial socio-economic impacts. PMID:23414369

Designing Computer Learning Environments for Engineering and Computer Science: The Scaffolded Knowledge Integration Framework.

ERIC Educational Resources Information Center

Linn, Marcia C.

1995-01-01

Describes a framework called scaffolded knowledge integration and illustrates how it guided the design of two successful course enhancements in the field of computer science and engineering: the LISP Knowledge Integration Environment and the spatial reasoning environment. (101 references) (Author/MKR)

Adapting Technological Pedagogical Content Knowledge Framework to Teach Mathematics

ERIC Educational Resources Information Center

Getenet, Seyum Tekeher

2017-01-01

The technological pedagogical content knowledge framework is increasingly in use by educational technology researcher as a generic description of the knowledge requirements for teachers using technology in all subjects. This study describes the development of a mathematics specific variety of the technological pedagogical content knowledge…

Knowledge and Power in the Technology Classroom: A Framework for Studying Teachers and Students in Action

ERIC Educational Resources Information Center

Danielsson, Anna T.; Berge, Maria; Lidar, Malena

2018-01-01

The purpose of this paper is to develop and illustrate an analytical framework for exploring how relations between knowledge and power are constituted in science and technology classrooms. In addition, the empirical purpose of this paper is to explore how disciplinary knowledge and knowledge-making are constituted in teacher-student interactions.…

A Novel Multi-Class Ensemble Model for Classifying Imbalanced Biomedical Datasets

NASA Astrophysics Data System (ADS)

Bikku, Thulasi; Sambasiva Rao, N., Dr; Rao, Akepogu Ananda, Dr

2017-08-01

This paper mainly focuseson developing aHadoop based framework for feature selection and classification models to classify high dimensionality data in heterogeneous biomedical databases. Wide research has been performing in the fields of Machine learning, Big data and Data mining for identifying patterns. The main challenge is extracting useful features generated from diverse biological systems. The proposed model can be used for predicting diseases in various applications and identifying the features relevant to particular diseases. There is an exponential growth of biomedical repositories such as PubMed and Medline, an accurate predictive model is essential for knowledge discovery in Hadoop environment. Extracting key features from unstructured documents often lead to uncertain results due to outliers and missing values. In this paper, we proposed a two phase map-reduce framework with text preprocessor and classification model. In the first phase, mapper based preprocessing method was designed to eliminate irrelevant features, missing values and outliers from the biomedical data. In the second phase, a Map-Reduce based multi-class ensemble decision tree model was designed and implemented in the preprocessed mapper data to improve the true positive rate and computational time. The experimental results on the complex biomedical datasets show that the performance of our proposed Hadoop based multi-class ensemble model significantly outperforms state-of-the-art baselines.

Redefining responsible research and innovation for the advancement of biobanking and biomedical research

PubMed Central

Yu, Helen

2016-01-01

Abstract One of the core objectives of responsible research and innovation (RRI) is to maximize the value of publicly funded research so that it may be returned to benefit society. However, while RRI encourages innovation through societal engagement, it can give rise to complex and previously untested issues that challenge the existing legal frameworks on intellectual property (IP) and public entitlement to benefits of research. In the case of biobanking, the personal nature of human biological materials and often altruistic intention of participants to donate samples intensifies the need to adhere to RRI principles with respect to the research, development, and commercialization of innovations derived from biobanks. However, stakeholders participate and collaborate with others in the innovation process to fulfill their own agenda. Without IP to safeguard investments in R&D, stakeholders may hesitate to contribute to the translation of discoveries into innovations. To realize the public benefit objective, RRI principles must protect the interests of stakeholders involved in the translation and commercialization of knowledge. This article explores the seemingly contradictory and competing objectives of open science and commercialization and proposes a holistic innovation framework directed at improving RRI practice for positive impact on obtaining the optimal social and economic values from research. PMID:28852540

Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data

PubMed Central

Repetski, Stephen; Venkataraman, Girish; Che, Anney; Luke, Brian T.; Girard, F. Pascal; Stephens, Robert M.

2013-01-01

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework. PMID:24312478

A neotropical Miocene pollen database employing image-based search and semantic modeling1

PubMed Central

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W.; Jaramillo, Carlos; Shyu, Chi-Ren

2014-01-01

• Premise of the study: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Methods: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Results: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Discussion: Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery. PMID:25202648

Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition.

PubMed

Wang, Qian; Gosik, Kirk; Xing, Sujuan; Jiang, Libo; Sun, Lidan; Chinchilli, Vernon M; Wu, Rongling

2017-03-01

Epigenetic reprogramming is thought to play a critical role in maintaining the normal development of embryos. How the methylation state of paternal and maternal genomes regulates embryogenesis depends on the interaction and coordination of the gametes of two sexes. While there is abundant research in exploring the epigenetic interactions of sperms and oocytes, a knowledge gap exists in the mechanistic quantitation of these interactions and their impact on embryo development. This review aims at formulating a modeling framework to address this gap through the integration and synthesis of evolutionary game theory and the latest discoveries of the epigenetic control of embryo development by next-generation sequencing. This framework, named epigenetic game theory or epiGame, views embryogenesis as an ecological system in which two highly distinct and specialized gametes coordinate through either cooperation or competition, or both, to maximize the fitness of embryos under Darwinian selection. By implementing a system of ordinary differential equations, epiGame quantifies the pattern and relative magnitude of the methylation effects on embryogenesis by the mechanisms of cooperation and competition. epiGame may gain new insight into reproductive biology and can be potentially applied to design personalized medicines for genetic disorder intervention. Copyright © 2016 Elsevier B.V. All rights reserved.

Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data.

PubMed

Mudunuri, Uma S; Khouja, Mohamad; Repetski, Stephen; Venkataraman, Girish; Che, Anney; Luke, Brian T; Girard, F Pascal; Stephens, Robert M

2013-01-01

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.

Disclosing incidental findings in brain research: the rights of minors in decision-making.

PubMed

Di Pietro, Nina C; Illes, Judy

2013-11-01

MRI is used routinely in research with children to generate new knowledge about brain development. The detection of unexpected brain abnormalities (incidental findings; IFs) in these studies presents unique challenges. While key issues surrounding incidence and significance, duty of care, and burden of disclosure have been addressed substantially for adults, less empirical data and normative analyses exist for minors who participate in minimal risk research. To identify ethical concerns and fill existing gaps, we conducted a comprehensive review of papers that focused explicitly on the discovery of IFs in minors. The discourse in the 21 papers retrieved for this analysis amply covered practical issues such as informed consent and screening, difficulties in ascertaining clinical significance, the economic costs and burden of responsibility on researchers, and risks (physical or psychological). However, we found little discussion about the involvement of minors in decisions about disclosure of IFs in the brain, especially for IFs of low clinical significance. In response, we propose a framework for managing IFs that integrates practical considerations with explicit appreciation of rights along the continuum of maturity. This capacity-adjusted framework emphasizes the importance of involving competent minors and respecting their right to make decisions about disclosure. Copyright © 2013 Wiley Periodicals, Inc.

Serendipity: Accidental Discoveries in Science

NASA Astrophysics Data System (ADS)

Roberts, Royston M.

1989-06-01

Many of the things discovered by accident are important in our everyday lives: Teflon, Velcro, nylon, x-rays, penicillin, safety glass, sugar substitutes, and polyethylene and other plastics. And we owe a debt to accident for some of our deepest scientific knowledge, including Newton's theory of gravitation, the Big Bang theory of Creation, and the discovery of DNA. Even the Rosetta Stone, the Dead Sea Scrolls, and the ruins of Pompeii came to light through chance. This book tells the fascinating stories of these and other discoveries and reveals how the inquisitive human mind turns accident into discovery. Written for the layman, yet scientifically accurate, this illuminating collection of anecdotes portrays invention and discovery as quintessentially human acts, due in part to curiosity, perserverance, and luck.

Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes

PubMed Central

2015-01-01

Self-optimization of chemical reactions enables faster optimization of reaction conditions or discovery of molecules with required target properties. The technology of self-optimization has been expanded to discovery of new process recipes for manufacture of complex functional products. A new machine-learning algorithm, specifically designed for multiobjective target optimization with an explicit aim to minimize the number of “expensive” experiments, guides the discovery process. This “black-box” approach assumes no a priori knowledge of chemical system and hence particularly suited to rapid development of processes to manufacture specialist low-volume, high-value products. The approach was demonstrated in discovery of process recipes for a semibatch emulsion copolymerization, targeting a specific particle size and full conversion. PMID:26435638

Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry.

PubMed

Anguera, A; Barreiro, J M; Lara, J A; Lizcano, D

2016-01-01

One of the major challenges in the medical domain today is how to exploit the huge amount of data that this field generates. To do this, approaches are required that are capable of discovering knowledge that is useful for decision making in the medical field. Time series are data types that are common in the medical domain and require specialized analysis techniques and tools, especially if the information of interest to specialists is concentrated within particular time series regions, known as events. This research followed the steps specified by the so-called knowledge discovery in databases (KDD) process to discover knowledge from medical time series derived from stabilometric (396 series) and electroencephalographic (200) patient electronic health records (EHR). The view offered in the paper is based on the experience gathered as part of the VIIP project. Knowledge discovery in medical time series has a number of difficulties and implications that are highlighted by illustrating the application of several techniques that cover the entire KDD process through two case studies. This paper illustrates the application of different knowledge discovery techniques for the purposes of classification within the above domains. The accuracy of this application for the two classes considered in each case is 99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG) domain and 99.4% and 99.1% for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results than other traditional neural network-based classification techniques.

Synthesis of Survey Questions That Accurately Discriminate the Elements of the TPACK Framework

ERIC Educational Resources Information Center

Jaikaran-Doe, Seeta; Doe, Peter Edward

2015-01-01

A number of validated survey instruments for assessing technological pedagogical content knowledge (TPACK) do not accurately discriminate between the seven elements of the TPACK framework particularly technological content knowledge (TCK) and technological pedagogical knowledge (TPK). By posing simple questions that assess technological,…

Knowledge Management ERP Curriculum Design/Mapping (Theory and Development Tools)

ERIC Educational Resources Information Center

Swanson, Zane; Hepner, Michelle

2011-01-01

This study proposes a knowledge management framework for developing and managing enterprise resource planning (ERP) curriculum within business schools. Both theory and a practical implementation are addressed. The knowledge management (KM) framework has two components which utilize ERP from a big picture curriculum overview and a ground level…

Technological Pedagogical Content Knowledge -- A Review of the Literature

ERIC Educational Resources Information Center

Voogt, J.; Fisser, P.; Roblin, N. Pareja; Tondeur, J.; van Braak, J.

2013-01-01

Technological Pedagogical Content Knowledge (TPACK) has been introduced as a conceptual framework for the knowledge base teachers need to effectively teach with technology. The framework stems from the notion that technology integration in a specific educational context benefits from a careful alignment of content, pedagogy and the potential of…

Communication, Constructivism, and Transfer of Knowledge in the Education of Bilingual Learners.

ERIC Educational Resources Information Center

Olivares, Rafael A.

2002-01-01

Discusses a theoretical framework to educate bilingual learners that links the communicative approach and the constructivist approach to learning with the transfer of knowledge from one language to another. The framework is illustrated in the communication, constructivism, and transference of knowledge (CCT) model where bilingual students use…

The applicability of the UK Public Health Skills and Knowledge Framework to the practitioner workforce: lessons for competency framework development.

PubMed

Shickle, Darren; Stroud, Laura; Day, Matthew; Smith, Kevin

2018-06-05

Many countries have developed competency frameworks for public health practice. While the number of competencies vary, frameworks cover similar knowledge and skills although they are not explicitly based on competency theory. A total of 15 qualitative group interviews (of up to six people), were conducted with 51 public health practitioners in 8 local authorities to assess the extent to which practitioners utilize competencies defined within the UK Public Health Skills and Knowledge Framework (PHSKF). Framework analysis was applied to the transcribed interviews. The overall framework was seen positively although no participants had previously read or utilized the PHSKF. Most could provide evidence, although some PHSKF competencies required creative thinking to fit expectations of practitioners and to reflect variation across the domains of practice which are impacted by job role and level of seniority. Evidence from previous NHS jobs or education may be needed as some competencies were not regularly utilized within their current local authority role. Further development of the PHSKF is required to provide guidance on how it should be used for practitioners and other members of the public health workforce. Empirical research can help benchmark knowledge/skills for workforce levels so improving the utility of competency frameworks.

Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials.

PubMed

Daza, Eric J

2018-02-01

Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis.We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful. Schattauer GmbH.

A General Framework for Discovery and Classification in Astronomy

NASA Astrophysics Data System (ADS)

Dick, Steven J.

2012-09-01

An analysis of the discovery of 82 classes of astronomical objects reveals an extended structure of discovery, consisting of detection, interpretation and understanding, each with its own nuances and a microstructure including conceptual, technological and social roles. This is true with a remarkable degree of consistency over the last 400 years of telescopic astronomy, ranging from Galileo's discovery of satellites, planetary rings and star clusters, to the discovery of quasars and pulsars. Telescopes have served as ``engines of discovery'' in several ways, ranging from telescope size and sensitivity (planetary nebulae and spiral nebulae), to specialized detectors (TNOs) and the opening of the electromagnetic spectrum for astronomy (pulsars, pulsar planets, and most active galaxies). A few classes (radiation belts, the solar wind and cosmic rays) were initially discovered without the telescope. Classification also plays an important role in discovery. While it might seem that classification marks the end of discovery, or a post-discovery phase, in fact it often marks the beginning, even a pre-discovery phase. Nowhere is this more clearly seen than in the classification of stellar spectra, long before dwarfs, giants and supergiants were known, or their evolutionary sequence recognized. Classification may also be part of a post-discovery phase, as in the MK system of stellar classification, constructed after the discovery of stellar luminosity classes. Some classes are declared rather than detected, as in the case of gas and ice giant planets, and, infamously, Pluto as a dwarf planet. Others are inferred rather than detected, including most classes of stars.

Equation Discovery for Model Identification in Respiratory Mechanics of the Mechanically Ventilated Human Lung

NASA Astrophysics Data System (ADS)

Ganzert, Steven; Guttmann, Josef; Steinmann, Daniel; Kramer, Stefan

Lung protective ventilation strategies reduce the risk of ventilator associated lung injury. To develop such strategies, knowledge about mechanical properties of the mechanically ventilated human lung is essential. This study was designed to develop an equation discovery system to identify mathematical models of the respiratory system in time-series data obtained from mechanically ventilated patients. Two techniques were combined: (i) the usage of declarative bias to reduce search space complexity and inherently providing the processing of background knowledge. (ii) A newly developed heuristic for traversing the hypothesis space with a greedy, randomized strategy analogical to the GSAT algorithm. In 96.8% of all runs the applied equation discovery system was capable to detect the well-established equation of motion model of the respiratory system in the provided data. We see the potential of this semi-automatic approach to detect more complex mathematical descriptions of the respiratory system from respiratory data.

Antisense oligonucleotide technologies in drug discovery.

PubMed

Aboul-Fadl, Tarek

2006-09-01

The principle of antisense oligonucleotide (AS-OD) technologies is based on the specific inhibition of unwanted gene expression by blocking mRNA activity. It has long appeared to be an ideal strategy to leverage new genomic knowledge for drug discovery and development. In recent years, AS-OD technologies have been widely used as potent and promising tools for this purpose. There is a rapid increase in the number of antisense molecules progressing in clinical trials. AS-OD technologies provide a simple and efficient approach for drug discovery and development and are expected to become a reality in the near future. This editorial describes the established and emerging AS-OD technologies in drug discovery.

100 years of elementary particles [Beam Line, vol. 27, issue 1, Spring 1997

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

Pais, Abraham; Weinberg, Steven; Quigg, Chris

1997-04-01

This issue of Beam Line commemorates the 100th anniversary of the April 30, 1897 report of the discovery of the electron by J.J. Thomson and the ensuing discovery of other subatomic particles. In the first three articles, theorists Abraham Pais, Steven Weinberg, and Chris Quigg provide their perspectives on the discoveries of elementary particles as well as the implications and future directions resulting from these discoveries. In the following three articles, Michael Riordan, Wolfgang Panofsky, and Virginia Trimble apply our knowledge about elementary particles to high-energy research, electronics technology, and understanding the origin and evolution of our Universe.

100 years of Elementary Particles [Beam Line, vol. 27, issue 1, Spring 1997

DOE R&D Accomplishments Database

Pais, Abraham; Weinberg, Steven; Quigg, Chris; Riordan, Michael; Panofsky, Wolfgang K. H.; Trimble, Virginia

1997-04-01

This issue of Beam Line commemorates the 100th anniversary of the April 30, 1897 report of the discovery of the electron by J.J. Thomson and the ensuing discovery of other subatomic particles. In the first three articles, theorists Abraham Pais, Steven Weinberg, and Chris Quigg provide their perspectives on the discoveries of elementary particles as well as the implications and future directions resulting from these discoveries. In the following three articles, Michael Riordan, Wolfgang Panofsky, and Virginia Trimble apply our knowledge about elementary particles to high-energy research, electronics technology, and understanding the origin and evolution of our Universe.

A knowledgebase system to enhance scientific discovery: Telemakus

PubMed Central

Fuller, Sherrilynne S; Revere, Debra; Bugni, Paul F; Martin, George M

2004-01-01

Background With the rapid expansion of scientific research, the ability to effectively find or integrate new domain knowledge in the sciences is proving increasingly difficult. Efforts to improve and speed up scientific discovery are being explored on a number of fronts. However, much of this work is based on traditional search and retrieval approaches and the bibliographic citation presentation format remains unchanged. Methods Case study. Results The Telemakus KnowledgeBase System provides flexible new tools for creating knowledgebases to facilitate retrieval and review of scientific research reports. In formalizing the representation of the research methods and results of scientific reports, Telemakus offers a potential strategy to enhance the scientific discovery process. While other research has demonstrated that aggregating and analyzing research findings across domains augments knowledge discovery, the Telemakus system is unique in combining document surrogates with interactive concept maps of linked relationships across groups of research reports. Conclusion Based on how scientists conduct research and read the literature, the Telemakus KnowledgeBase System brings together three innovations in analyzing, displaying and summarizing research reports across a domain: (1) research report schema, a document surrogate of extracted research methods and findings presented in a consistent and structured schema format which mimics the research process itself and provides a high-level surrogate to facilitate searching and rapid review of retrieved documents; (2) research findings, used to index the documents, allowing searchers to request, for example, research studies which have studied the relationship between neoplasms and vitamin E; and (3) visual exploration interface of linked relationships for interactive querying of research findings across the knowledgebase and graphical displays of what is known as well as, through gaps in the map, what is yet to be tested. The rationale and system architecture are described and plans for the future are discussed. PMID:15507158

An Exploration of the Utility of a Knowledge Utilization Framework to Study the Gap between Reading Disabilities Research and Practice

ERIC Educational Resources Information Center

Davidson, Katherine; Nowicki, Elizabeth

2012-01-01

This pre-pilot study explored the usefulness of a knowledge utilization framework comprised of Knott and Wildavsky's (1980) seven stages of knowledge use and Stone's (2002) three routes to knowledge use to investigate the gap between reading disabilities research and teachers' self-reported use of that research. Semi-structured interviews of ten…

A network model of knowledge accumulation through diffusion and upgrade

NASA Astrophysics Data System (ADS)

Zhuang, Enyu; Chen, Guanrong; Feng, Gang

2011-07-01

In this paper, we introduce a model to describe knowledge accumulation through knowledge diffusion and knowledge upgrade in a multi-agent network. Here, knowledge diffusion refers to the distribution of existing knowledge in the network, while knowledge upgrade means the discovery of new knowledge. It is found that the population of the network and the number of each agent’s neighbors affect the speed of knowledge accumulation. Four different policies for updating the neighboring agents are thus proposed, and their influence on the speed of knowledge accumulation and the topology evolution of the network are also studied.

Interfaith Education: An Islamic Perspective

ERIC Educational Resources Information Center

Pallavicini, Yahya Sergio Yahe

2016-01-01

According to a teaching of the Prophet Muhammad, "the quest for knowledge is the duty of each Muslim, male or female", where knowledge is meant as the discovery of the real value of things and of oneself in relationship with the world in which God has placed us. This universal dimension of knowledge is in fact a wealth of wisdom of the…

A roadmap for knowledge exchange and mobilization research in conservation and natural resource management.

PubMed

Nguyen, Vivian M; Young, Nathan; Cooke, Steven J

2017-08-01

Scholars across all disciplines have long been interested in how knowledge moves within and beyond their community of peers. Rapid environmental changes and calls for sustainable management practices mean the best knowledge possible is needed to inform decisions, policies, and practices to protect biodiversity and sustainably manage vulnerable natural resources. Although the conservation literature on knowledge exchange (KE) and knowledge mobilization (KM) has grown in recent years, much of it is based on context-specific case studies. This presents a challenge for learning cumulative lessons from KE and KM research and thus effectively using knowledge in conservation and natural resources management. Although continued research on the gap between knowledge and action is valuable, overarching conceptual frameworks are now needed to enable summaries and comparisons across diverse KE-KM research. We propose a knowledge-action framework that provides a conceptual roadmap for future research and practice in KE/KM with the aim of synthesizing lessons learned from contextual case studies and guiding the development and testing of hypotheses in this domain. Our knowledge-action framework has 3 elements that occur at multiple levels and scales: knowledge production (e.g., academia and government), knowledge mediation (e.g., knowledge networks, actors, relational dimension, and contextual dimension), and knowledge-based action (e.g., instrumental, symbolic, and conceptual). The framework integrates concepts from the sociology of science in particular, and serves as a guide to further comprehensive understanding of knowledge exchange and mobilization in conservation and sustainable natural resource management. © 2016 Society for Conservation Biology.

Integrating semantic web technologies and geospatial catalog services for geospatial information discovery and processing in cyberinfrastructure

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

Yue, Peng; Gong, Jianya; Di, Liping

Abstract A geospatial catalogue service provides a network-based meta-information repository and interface for advertising and discovering shared geospatial data and services. Descriptive information (i.e., metadata) for geospatial data and services is structured and organized in catalogue services. The approaches currently available for searching and using that information are often inadequate. Semantic Web technologies show promise for better discovery methods by exploiting the underlying semantics. Such development needs special attention from the Cyberinfrastructure perspective, so that the traditional focus on discovery of and access to geospatial data can be expanded to support the increased demand for processing of geospatial information andmore » discovery of knowledge. Semantic descriptions for geospatial data, services, and geoprocessing service chains are structured, organized, and registered through extending elements in the ebXML Registry Information Model (ebRIM) of a geospatial catalogue service, which follows the interface specifications of the Open Geospatial Consortium (OGC) Catalogue Services for the Web (CSW). The process models for geoprocessing service chains, as a type of geospatial knowledge, are captured, registered, and discoverable. Semantics-enhanced discovery for geospatial data, services/service chains, and process models is described. Semantic search middleware that can support virtual data product materialization is developed for the geospatial catalogue service. The creation of such a semantics-enhanced geospatial catalogue service is important in meeting the demands for geospatial information discovery and analysis in Cyberinfrastructure.« less

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 8: The role of the information intermediary in the diffusion of aerospace knowledge

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Kennedy, John M.; Barclay, Rebecca O.

1990-01-01

The United States aerospace industry is experiencing profound changes created by a combination of domestic actions and circumstances such as airline deregulation. Other changes result from external trends such as emerging foreign competition. These circumstances intensify the need to understand the production, transfer, and utilization of knowledge as a precursor to the rapid diffusion of technology. Presented here is a conceptual framework for understanding the diffusion of technology. A conceptual framework is given for understanding the diffusion of aerospace knowledge. The framework focuses on the information channels and members of the social system associated with the aerospace knowledge diffusion process, placing particular emphasis on aerospace librarians as information intermediaries.

How music training enhances working memory: a cerebrocerebellar blending mechanism that can lead equally to scientific discovery and therapeutic efficacy in neurological disorders.

PubMed

Vandervert, Larry

2015-01-01

Following in the vein of studies that concluded that music training resulted in plastic changes in Einstein's cerebral cortex, controlled research has shown that music training (1) enhances central executive attentional processes in working memory, and (2) has also been shown to be of significant therapeutic value in neurological disorders. Within this framework of music training-induced enhancement of central executive attentional processes, the purpose of this article is to argue that: (1) The foundational basis of the central executive begins in infancy as attentional control during the establishment of working memory, (2) In accordance with Akshoomoff, Courchesne and Townsend's and Leggio and Molinari's cerebellar sequence detection and prediction models, the rigors of volitional control demands of music training can enhance voluntary manipulation of information in thought and movement, (3) The music training-enhanced blending of cerebellar internal models in working memory as can be experienced as intuition in scientific discovery (as Einstein often indicated) or, equally, as moments of therapeutic advancement toward goals in the development of voluntary control in neurological disorders, and (4) The blending of internal models as in (3) thus provides a mechanism by which music training enhances central executive processes in working memory that can lead to scientific discovery and improved therapeutic outcomes in neurological disorders. Within the framework of Leggio and Molinari's cerebellar sequence detection model, it is determined that intuitive steps forward that occur in both scientific discovery and during therapy in those with neurological disorders operate according to the same mechanism of adaptive error-driven blending of cerebellar internal models. It is concluded that the entire framework of the central executive structure of working memory is a product of the cerebrocerebellar system which can, through the learning of internal models, incorporate the multi-dimensional rigor and volitional-control demands of music training and, thereby, enhance voluntary control. It is further concluded that this cerebrocerebellar view of the music training-induced enhancement of central executive control in working memory provides a needed mechanism to explain both the highest level of scientific discovery and the efficacy of music training in the remediation of neurological impairments.

77 FR 75459 - Self-Regulatory Organizations; BATS Exchange, Inc.; Notice of Filing of a Proposed Rule Change To...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-20

... both sides would participate in an Exchange Auction, this proposed change would aid in price discovery... auction price. This proposed change would aid in price discovery and help to reduce the likelihood of... Sell Shares and, therefore, a User would never have complete knowledge of liquidity available on both...

Essential Skills and Knowledge for Troubleshooting E-Resources Access Issues in a Web-Scale Discovery Environment

ERIC Educational Resources Information Center

Carter, Sunshine; Traill, Stacie

2017-01-01

Electronic resource access troubleshooting is familiar work in most libraries. The added complexity introduced when a library implements a web-scale discovery service, however, creates a strong need for well-organized, rigorous training to enable troubleshooting staff to provide the best service possible. This article outlines strategies, tools,…

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

Mothers' Initial Discovery of Childhood Disability: Exploring Maternal Identification of Developmental Issues in Young Children

ERIC Educational Resources Information Center

Silbersack, Elionora W.

2014-01-01

The purpose of this qualitative study was to expand the scarce information available on how mothers first observe their children's early development, assess potential problems, and then come to recognize their concerns. In-depth knowledge about mothers' perspectives on the discovery process can help social workers to promote identification of…

Augmented Reality-Based Simulators as Discovery Learning Tools: An Empirical Study

ERIC Educational Resources Information Center

Ibáñez, María-Blanca; Di-Serio, Ángela; Villarán-Molina, Diego; Delgado-Kloos, Carlos

2015-01-01

This paper reports empirical evidence on having students use AR-SaBEr, a simulation tool based on augmented reality (AR), to discover the basic principles of electricity through a series of experiments. AR-SaBEr was enhanced with knowledge-based support and inquiry-based scaffolding mechanisms, which proved useful for discovery learning in…

76 FR 36320 - Rules of Practice in Proceedings Relative to False Representation and Lottery Orders

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-22

... officers. 952.18 Evidence. 952.19 Subpoenas. 952.20 Witness fees. 952.21 Discovery. 952.22 Transcript. 952..., motions, proposed orders, and other documents for the record. Discovery need not be filed except as may be... witnesses, that the statement correctly states the witness's opinion or knowledge concerning the matters in...

Making the Long Tail Visible: Social Networking Sites and Independent Music Discovery

ERIC Educational Resources Information Center

Gaffney, Michael; Rafferty, Pauline

2009-01-01

Purpose: The purpose of this paper is to investigate users' knowledge and use of social networking sites and folksonomies to discover if social tagging and folksonomies, within the area of independent music, aid in its information retrieval and discovery. The sites examined in this project are MySpace, Lastfm, Pandora and Allmusic. In addition,…

The operant-respondent distinction: Future directions

PubMed Central

Pear, Joseph J.; Eldridge, Gloria D.

1984-01-01

The operant-respondent distinction has provided a major organizing framework for the data generated through the experimental analysis of behavior. Problems have been encountered, however, in using it as an explanatory concept for such phenomena as avoidance and conditioned suppression. Data now exist that do not fit neatly into the framework. Moreover, the discovery of autoshaping has highlighted difficulties in isolating the two types of behavior and conditioning. Despite these problems, the operant-respondent framework remains the most successful paradigm currently available for organizing behavioral data. Research and theoretical efforts should therefore probably be directed to modifying the framework to account for disparate data. PMID:16812402

Arden Syntax Clinical Foundation Framework for Event Monitoring in Intensive Care Units: Report on a Pilot Study.

PubMed

de Bruin, Jeroen S; Zeckl, Julia; Adlassnig, Katharina; Blacky, Alexander; Koller, Walter; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2017-01-01

The creation of clinical decision support systems has received a strong impulse over the last years, but their integration into a clinical routine has lagged behind, partly due to a lack of interoperability and trust by physicians. We report on the implementation of a clinical foundation framework in Arden Syntax, comprising knowledge units for (a) preprocessing raw clinical data, (b) the determination of single clinical concepts, and (c) more complex medical knowledge, which can be modeled through the composition and configuration of knowledge units in this framework. Thus, it can be tailored to clinical institutions or patients' caregivers. In the present version, we integrated knowledge units for several infection-related clinical concepts into the framework and developed a clinical event monitoring system over the framework that employs three different scenarios for monitoring clinical signs of bloodstream infection. The clinical event monitoring system was tested using data from intensive care units at Vienna General Hospital, Austria.

A web Accessible Framework for Discovery, Visualization and Dissemination of Polar Data

NASA Astrophysics Data System (ADS)

Kirsch, P. J.; Breen, P.; Barnes, T. D.

2007-12-01

A web accessible information framework, currently under development within the Physical Sciences Division of the British Antarctic Survey is described. The datasets accessed are generally heterogeneous in nature from fields including space physics, meteorology, atmospheric chemistry, ice physics, and oceanography. Many of these are returned in near real time over a 24/7 limited bandwidth link from remote Antarctic Stations and ships. The requirement is to provide various user groups - each with disparate interests and demands - a system incorporating a browsable and searchable catalogue; bespoke data summary visualization, metadata access facilities and download utilities. The system allows timely access to raw and processed datasets through an easily navigable discovery interface. Once discovered, a summary of the dataset can be visualized in a manner prescribed by the particular projects and user communities or the dataset may be downloaded, subject to accessibility restrictions that may exist. In addition, access to related ancillary information including software, documentation, related URL's and information concerning non-electronic media (of particular relevance to some legacy datasets) is made directly available having automatically been associated with a dataset during the discovery phase. Major components of the framework include the relational database containing the catalogue, the organizational structure of the systems holding the data - enabling automatic updates of the system catalogue and real-time access to data -, the user interface design, and administrative and data management scripts allowing straightforward incorporation of utilities, datasets and system maintenance.

Designing and Implementing an Integrated Technological Pedagogical Science Knowledge Framework for Science Teachers Professional Development

ERIC Educational Resources Information Center

Jimoyiannis, Athanassios

2010-01-01

This paper reports on the design and the implementation of the Technological Pedagogical Science Knowledge (TPASK), a new model for science teachers professional development built on an integrated framework determined by the Technological Pedagogical Content Knowledge (TPACK) model and the authentic learning approach. The TPASK curriculum…

Transnational Corporations and Strategic Challenges: An Analysis of Knowledge Flows and Competitive Advantage

ERIC Educational Resources Information Center

de Pablos, Patricia Ordonez

2006-01-01

Purpose: The purpose of this paper is to analyse knowledge transfers in transnational corporations. Design/methodology/approach: The paper develops a conceptual framework for the analysis of knowledge flow transfers in transnationals. Based on this theoretical framework, the paper propose's research hypotheses and builds a causal model that links…

Conceptualization of Depth of Vocabulary Knowledge with Academic Reading Comprehension

ERIC Educational Resources Information Center

Hasan, Md. Kamrul; Shabdin, Ahmad Affendi

2016-01-01

The present study embodies a conceptual framework, and it studies the concept regarding the depth of vocabulary knowledge. Literature review is employed as a foundation for developing the conceptual framework for the present study. The current study suggests that different dimensions of depth of vocabulary knowledge, namely paradigmatic relations,…

Joint Interactions in Large Online Knowledge Communities: The A[subscript 3]C Framework

ERIC Educational Resources Information Center

Jeong, Heisawn; Cress, Ulrike; Moskaliuk, Johannes; Kimmerle, Joachim

2017-01-01

Social interaction is crucial for understanding individual and collective processes in knowledge communities. We describe how technology has changed the way people interact in large communities. Building on this description, we propose a framework that distinguishes four types of joint interactions in online knowledge communities: Attendance,…

Promoting Teachers' Learning and Knowledge Building in a Socio-Technical System

ERIC Educational Resources Information Center

Tammets, Kairit; Pata, Kai; Laanpere, Mart

2013-01-01

The study proposes a way in which the learning and knowledge building (LKB) framework, which is consistent with the knowledge conversion phases proposed by Nonaka and Takeuchi, supports teachers' informal and self-directed workplace learning. An LKB framework in a socio-technical system was developed to support professional development in an…

Interpretation of Radiological Images: Towards a Framework of Knowledge and Skills

ERIC Educational Resources Information Center

van der Gijp, A.; van der Schaaf, M. F.; van der Schaaf, I. C.; Huige, J. C. B. M.; Ravesloot, C. J.; van Schaik, J. P. J.; ten Cate, Th. J.

2014-01-01

The knowledge and skills that are required for radiological image interpretation are not well documented, even though medical imaging is gaining importance. This study aims to develop a comprehensive framework of knowledge and skills, required for two-dimensional and multiplanar image interpretation in radiology. A mixed-method study approach was…

Conceptualizing a Framework for Advanced Placement Statistics Teaching Knowledge

ERIC Educational Resources Information Center

Haines, Brenna

2015-01-01

The purpose of this article is to sketch a conceptualization of a framework for Advanced Placement (AP) Statistics Teaching Knowledge. Recent research continues to problematize the lack of knowledge and preparation among secondary level statistics teachers. The College Board's AP Statistics course continues to grow and gain popularity, but is a…

Professional development for primary science teaching in Thailand: Knowledge, orientations, and practices of professional developers and professional development participants

NASA Astrophysics Data System (ADS)

Musikul, Kusalin

The purpose of this study was to examine an entire PD project as a case to understand the dynamic nature of science PD in a holistic manner. I used a pedagogical content knowledge model by Magnusson, Krajcik, and Borko (1999) as my theoretical framework in examining the professional developers' and teacher participants' knowledge, orientation, and practice for professional development and elementary science teaching. The case study is my research tradition; I used grounded theory for data analysis. The primary data sources were interview, card sort activity, and observation field notes collected during the PD and subsequently in teacher participants' classrooms. Secondary data sources were documents and artifacts that I collected from the professional developers and teachers. An analysis of the data led me to interpret the following findings: (a) the professional developers displayed multiple orientations. These orientations included activity-driven, didactic, discovery, and pedagogy-driven orientations. The orientations that were found among the professional developers deviated from the reformed Thai Science Education Standards; (b) the professional developers had limited PCK for PD, which were knowledge of teachers' learning, knowledge of PD strategies, knowledge of PD curriculum, and knowledge of assessment.; (c) the professional developers' knowledge and orientations influenced their decisions in selecting PD activities and teaching approaches; (d) their orientations and PCK as well as the time factor influenced the design and implementation of the professional development; (e) the elementary teachers displayed didactic, activity-driven, and academic rigor orientations. The orientations that the teachers displayed deviated from the reformed Thai Science Education Standards; and (f) the elementary teachers exhibited limited PCK. It is evident that the limitation of one type of knowledge resulted in an ineffective use of other components of PCK. This study demonstrates the nature of PD in the context of Thailand in a holistic view to understand knowledge, orientation, and implementation of professional developers and professional development participants. Furthermore, the findings have implications for professional development and professional developers in Thailand and include worldwide with respect to promoting sustain and intensive professional development and developing professional developers.

Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data

PubMed Central

2016-01-01

Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioral ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics. PMID:27851814

Discovery and introduction of a (3,18)-connected net as an ideal blueprint for the design of metal-organic frameworks.

PubMed

Guillerm, Vincent; Weseliński, Łukasz J; Belmabkhout, Youssef; Cairns, Amy J; D'Elia, Valerio; Wojtas, Łukasz; Adil, Karim; Eddaoudi, Mohamed

2014-08-01

Metal-organic frameworks (MOFs) are a promising class of porous materials because it is possible to mutually control their porous structure, composition and functionality. However, it is still a challenge to predict the network topology of such framework materials prior to their synthesis. Here we use a new rare earth (RE) nonanuclear carboxylate-based cluster as an 18-connected molecular building block to form a gea-MOF (gea-MOF-1) based on a (3,18)-connected net. We then utilized this gea net as a blueprint to design and assemble another MOF (gea-MOF-2). In gea-MOF-2, the 18-connected RE clusters are replaced by metal-organic polyhedra, peripherally functionalized so as to have the same connectivity as the RE clusters. These metal-organic polyhedra act as supermolecular building blocks when they form gea-MOF-2. The discovery of a (3,18)-connected MOF followed by deliberate transposition of its topology to a predesigned second MOF with a different chemical system validates the prospective rational design of MOFs.

Making Just Tenure and Promotion Decisions Using the Objective Knowledge Growth Framework

ERIC Educational Resources Information Center

Chitpin, Stephanie

2015-01-01

Purpose: The purpose of this paper is to utilize the Objective Knowledge Growth Framework (OKGF) to promote a better understanding of the evaluating tenure and promotion processes. Design/Methodology/Approach: A scenario is created to illustrate the concept of using OKGF. Findings: The framework aims to support decision makers in identifying the…

Compound annotation with real time cellular activity profiles to improve drug discovery.

PubMed

Fang, Ye

2016-01-01

In the past decade, a range of innovative strategies have been developed to improve the productivity of pharmaceutical research and development. In particular, compound annotation, combined with informatics, has provided unprecedented opportunities for drug discovery. In this review, a literature search from 2000 to 2015 was conducted to provide an overview of the compound annotation approaches currently used in drug discovery. Based on this, a framework related to a compound annotation approach using real-time cellular activity profiles for probe, drug, and biology discovery is proposed. Compound annotation with chemical structure, drug-like properties, bioactivities, genome-wide effects, clinical phenotypes, and textural abstracts has received significant attention in early drug discovery. However, these annotations are mostly associated with endpoint results. Advances in assay techniques have made it possible to obtain real-time cellular activity profiles of drug molecules under different phenotypes, so it is possible to generate compound annotation with real-time cellular activity profiles. Combining compound annotation with informatics, such as similarity analysis, presents a good opportunity to improve the rate of discovery of novel drugs and probes, and enhance our understanding of the underlying biology.

Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images

NASA Astrophysics Data System (ADS)

Stranieri, Andrew; Yearwood, John; Pham, Binh

1999-07-01

The development of data warehouses for the storage and analysis of very large corpora of medical image data represents a significant trend in health care and research. Amongst other benefits, the trend toward warehousing enables the use of techniques for automatically discovering knowledge from large and distributed databases. In this paper, we present an application design for knowledge discovery from databases (KDD) techniques that enhance the performance of the problem solving strategy known as case- based reasoning (CBR) for the diagnosis of radiological images. The problem of diagnosing the abnormality of the cervical spine is used to illustrate the method. The design of a case-based medical image diagnostic support system has three essential characteristics. The first is a case representation that comprises textual descriptions of the image, visual features that are known to be useful for indexing images, and additional visual features to be discovered by data mining many existing images. The second characteristic of the approach presented here involves the development of a case base that comprises an optimal number and distribution of cases. The third characteristic involves the automatic discovery, using KDD techniques, of adaptation knowledge to enhance the performance of the case based reasoner. Together, the three characteristics of our approach can overcome real time efficiency obstacles that otherwise mitigate against the use of CBR to the domain of medical image analysis.

Discovery learning model with geogebra assisted for improvement mathematical visual thinking ability

NASA Astrophysics Data System (ADS)

Juandi, D.; Priatna, N.

2018-05-01

The main goal of this study is to improve the mathematical visual thinking ability of high school student through implementation the Discovery Learning Model with Geogebra Assisted. This objective can be achieved through study used quasi-experimental method, with non-random pretest-posttest control design. The sample subject of this research consist of 62 senior school student grade XI in one of school in Bandung district. The required data will be collected through documentation, observation, written tests, interviews, daily journals, and student worksheets. The results of this study are: 1) Improvement students Mathematical Visual Thinking Ability who obtain learning with applied the Discovery Learning Model with Geogebra assisted is significantly higher than students who obtain conventional learning; 2) There is a difference in the improvement of students’ Mathematical Visual Thinking ability between groups based on prior knowledge mathematical abilities (high, medium, and low) who obtained the treatment. 3) The Mathematical Visual Thinking Ability improvement of the high group is significantly higher than in the medium and low groups. 4) The quality of improvement ability of high and low prior knowledge is moderate category, in while the quality of improvement ability in the high category achieved by student with medium prior knowledge.

No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects.

PubMed

Danchin, Antoine; Ouzounis, Christos; Tokuyasu, Taku; Zucker, Jean-Daniel

2018-07-01

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from 'the sequence tells the structure tells the function' fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader. © 2018 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Temporal data mining for the quality assessment of hemodialysis services.

PubMed

Bellazzi, Riccardo; Larizza, Cristiana; Magni, Paolo; Bellazzi, Roberto

2005-05-01

This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series automatically collected during hemodialysis sessions. Intelligent data analysis and temporal data mining techniques are applied to gain insight and to discover knowledge on the causes of unsatisfactory clinical results. In particular, two new methods for association rule discovery and temporal rule discovery are applied to the time series. Such methods exploit several pre-processing techniques, comprising data reduction, multi-scale filtering and temporal abstractions. We have analyzed the data of more than 5800 dialysis sessions coming from 43 different patients monitored for 19 months. The qualitative rules associating the outcome parameters and the measured variables were examined by the domain experts, which were able to distinguish between rules confirming available background knowledge and unexpected but plausible rules. The new methods proposed in the paper are suitable tools for knowledge discovery in clinical time series. Their use in the context of an auditing system for dialysis management helped clinicians to improve their understanding of the patients' behavior.

Pain Research Forum: application of scientific social media frameworks in neuroscience.

PubMed

Das, Sudeshna; McCaffrey, Patricia G; Talkington, Megan W T; Andrews, Neil A; Corlosquet, Stéphane; Ivinson, Adrian J; Clark, Tim

2014-01-01

Social media has the potential to accelerate the pace of biomedical research through online collaboration, discussions, and faster sharing of information. Focused web-based scientific social collaboratories such as the Alzheimer Research Forum have been successful in engaging scientists in open discussions of the latest research and identifying gaps in knowledge. However, until recently, tools to rapidly create such communities and provide high-bandwidth information exchange between collaboratories in related fields did not exist. We have addressed this need by constructing a reusable framework to build online biomedical communities, based on Drupal, an open-source content management system. The framework incorporates elements of Semantic Web technology combined with social media. Here we present, as an exemplar of a web community built on our framework, the Pain Research Forum (PRF) (http://painresearchforum.org). PRF is a community of chronic pain researchers, established with the goal of fostering collaboration and communication among pain researchers. Launched in 2011, PRF has over 1300 registered members with permission to submit content. It currently hosts over 150 topical news articles on research; more than 30 active or archived forum discussions and journal club features; a webinar series; an editor-curated weekly updated listing of relevant papers; and several other resources for the pain research community. All content is licensed for reuse under a Creative Commons license; the software is freely available. The framework was reused to develop other sites, notably the Multiple Sclerosis Discovery Forum (http://msdiscovery.org) and StemBook (http://stembook.org). Web-based collaboratories are a crucial integrative tool supporting rapid information transmission and translation in several important research areas. In this article, we discuss the success factors, lessons learned, and ongoing challenges in using PRF as a driving force to develop tools for online collaboration in neuroscience. We also indicate ways these tools can be applied to other areas and uses.

Metrics for comparing neuronal tree shapes based on persistent homology.

PubMed

Li, Yanjie; Wang, Dingkang; Ascoli, Giorgio A; Mitra, Partha; Wang, Yusu

2017-01-01

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities-Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework.

Metrics for comparing neuronal tree shapes based on persistent homology

PubMed Central

Li, Yanjie; Wang, Dingkang; Ascoli, Giorgio A.; Mitra, Partha

2017-01-01

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities—Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework. PMID:28809960

Pain Research Forum: application of scientific social media frameworks in neuroscience

PubMed Central

Das, Sudeshna; McCaffrey, Patricia G.; Talkington, Megan W. T.; Andrews, Neil A.; Corlosquet, Stéphane; Ivinson, Adrian J.; Clark, Tim

2014-01-01

Background: Social media has the potential to accelerate the pace of biomedical research through online collaboration, discussions, and faster sharing of information. Focused web-based scientific social collaboratories such as the Alzheimer Research Forum have been successful in engaging scientists in open discussions of the latest research and identifying gaps in knowledge. However, until recently, tools to rapidly create such communities and provide high-bandwidth information exchange between collaboratories in related fields did not exist. Methods: We have addressed this need by constructing a reusable framework to build online biomedical communities, based on Drupal, an open-source content management system. The framework incorporates elements of Semantic Web technology combined with social media. Here we present, as an exemplar of a web community built on our framework, the Pain Research Forum (PRF) (http://painresearchforum.org). PRF is a community of chronic pain researchers, established with the goal of fostering collaboration and communication among pain researchers. Results: Launched in 2011, PRF has over 1300 registered members with permission to submit content. It currently hosts over 150 topical news articles on research; more than 30 active or archived forum discussions and journal club features; a webinar series; an editor-curated weekly updated listing of relevant papers; and several other resources for the pain research community. All content is licensed for reuse under a Creative Commons license; the software is freely available. The framework was reused to develop other sites, notably the Multiple Sclerosis Discovery Forum (http://msdiscovery.org) and StemBook (http://stembook.org). Discussion: Web-based collaboratories are a crucial integrative tool supporting rapid information transmission and translation in several important research areas. In this article, we discuss the success factors, lessons learned, and ongoing challenges in using PRF as a driving force to develop tools for online collaboration in neuroscience. We also indicate ways these tools can be applied to other areas and uses. PMID:24653693

A framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps

NASA Astrophysics Data System (ADS)

Xu, Jin; Li, Zheng; Li, Shuliang; Zhang, Yanyan

2015-07-01

There is still a lack of effective paradigms and tools for analysing and discovering the contents and relationships of project knowledge contexts in the field of project management. In this paper, a new framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps under big data environments is proposed and developed. The conceptual paradigm, theoretical underpinning, extended topic model, and illustration examples of the ontology model for project knowledge maps are presented, with further research work envisaged.

Emerging Concepts and Methodologies in Cancer Biomarker Discovery.

PubMed

Lu, Meixia; Zhang, Jinxiang; Zhang, Lanjing

2017-01-01

Cancer biomarker discovery is a critical part of cancer prevention and treatment. Despite the decades of effort, only a small number of cancer biomarkers have been identified for and validated in clinical settings. Conceptual and methodological breakthroughs may help accelerate the discovery of additional cancer biomarkers, particularly their use for diagnostics. In this review, we have attempted to review the emerging concepts in cancer biomarker discovery, including real-world evidence, open access data, and data paucity in rare or uncommon cancers. We have also summarized the recent methodological progress in cancer biomarker discovery, such as high-throughput sequencing, liquid biopsy, big data, artificial intelligence (AI), and deep learning and neural networks. Much attention has been given to the methodological details and comparison of the methodologies. Notably, these concepts and methodologies interact with each other and will likely lead to synergistic effects when carefully combined. Newer, more innovative concepts and methodologies are emerging as the current emerging ones became mainstream and widely applied to the field. Some future challenges are also discussed. This review contributes to the development of future theoretical frameworks and technologies in cancer biomarker discovery and will contribute to the discovery of more useful cancer biomarkers.

Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry

PubMed Central

2017-01-01

The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein–protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called ‘linear model’, where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world. PMID:28875019

Choosing experiments to accelerate collective discovery

PubMed Central

Rzhetsky, Andrey; Foster, Jacob G.; Foster, Ian T.

2015-01-01

A scientist’s choice of research problem affects his or her personal career trajectory. Scientists’ combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity’s importance corresponds to its degree centrality, and a problem’s difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies. PMID:26554009

Using the Knowledge to Action Framework in practice: a citation analysis and systematic review.

PubMed

Field, Becky; Booth, Andrew; Ilott, Irene; Gerrish, Kate

2014-11-23

Conceptual frameworks are recommended as a way of applying theory to enhance implementation efforts. The Knowledge to Action (KTA) Framework was developed in Canada by Graham and colleagues in the 2000s, following a review of 31 planned action theories. The framework has two components: Knowledge Creation and an Action Cycle, each of which comprises multiple phases. This review sought to answer two questions: 'Is the KTA Framework used in practice? And if so, how?' This study is a citation analysis and systematic review. The index citation for the original paper was identified on three databases-Web of Science, Scopus and Google Scholar-with the facility for citation searching. Limitations of English language and year of publication 2006-June 2013 were set. A taxonomy categorising the continuum of usage was developed. Only studies applying the framework to implementation projects were included. Data were extracted and mapped against each phase of the framework for studies where it was integral to the implementation project. The citation search yielded 1,787 records. A total of 1,057 titles and abstracts were screened. One hundred and forty-six studies described usage to varying degrees, ranging from referenced to integrated. In ten studies, the KTA Framework was integral to the design, delivery and evaluation of the implementation activities. All ten described using the Action Cycle and seven referred to Knowledge Creation. The KTA Framework was enacted in different health care and academic settings with projects targeted at patients, the public, and nursing and allied health professionals. The KTA Framework is being used in practice with varying degrees of completeness. It is frequently cited, with usage ranging from simple attribution via a reference, through informing planning, to making an intellectual contribution. When the framework was integral to knowledge translation, it guided action in idiosyncratic ways and there was theory fidelity. Prevailing wisdom encourages the use of theories, models and conceptual frameworks, yet their application is less evident in practice. This may be an artefact of reporting, indicating that prospective, primary research is needed to explore the real value of the KTA Framework and similar tools.

Big Data Discovery and Access Services through NOAA OneStop

NASA Astrophysics Data System (ADS)

Casey, K. S.; Neufeld, D.; Ritchey, N. A.; Relph, J.; Fischman, D.; Baldwin, R.

2017-12-01

The NOAA OneStop Project was created as a pathfinder effort to to improve the discovery of, access to, and usability of NOAA's vast and diverse collection of big data. OneStop is led by the NOAA/NESDIS National Centers for Environmental Information (NCEI), and is seen as a key NESDIS contribution to NOAA's open data and data stewardship efforts. OneStop consists of an entire framework of services, from storage and interoperable access services at the base, through metadata and catalog services in the middle, to a modern user interface experience at the top. Importantly, it is an open framework where external tools and services can connect at whichever level is most appropriate. Since the beta release of the OneStop user interface at the 2016 Fall AGU meeting, significant progress has been made improving and modernizing many NOAA data collections to optimize their use within the framework. In addition, OneStop has made progress implementing robust metadata management and catalog systems at the collection and granule level and improving the user experience with the web interface. This progress will be summarized and the results of extensive user testing including professional usability studies will be reviewed. Key big data technologies supporting the framework will be presented and a community input sought on the future directions of the OneStop Project.

Interoperable Data Sharing for Diverse Scientific Disciplines

NASA Astrophysics Data System (ADS)

Hughes, John S.; Crichton, Daniel; Martinez, Santa; Law, Emily; Hardman, Sean

2016-04-01

For diverse scientific disciplines to interoperate they must be able to exchange information based on a shared understanding. To capture this shared understanding, we have developed a knowledge representation framework using ontologies and ISO level archive and metadata registry reference models. This framework provides multi-level governance, evolves independent of implementation technologies, and promotes agile development, namely adaptive planning, evolutionary development, early delivery, continuous improvement, and rapid and flexible response to change. The knowledge representation framework is populated through knowledge acquisition from discipline experts. It is also extended to meet specific discipline requirements. The result is a formalized and rigorous knowledge base that addresses data representation, integrity, provenance, context, quantity, and their relationships within the community. The contents of the knowledge base is translated and written to files in appropriate formats to configure system software and services, provide user documentation, validate ingested data, and support data analytics. This presentation will provide an overview of the framework, present the Planetary Data System's PDS4 as a use case that has been adopted by the international planetary science community, describe how the framework is being applied to other disciplines, and share some important lessons learned.

Applying knowledge-anchored hypothesis discovery methods to advance clinical and translational research: the OAMiner project

PubMed Central

Jackson, Rebecca D; Best, Thomas M; Borlawsky, Tara B; Lai, Albert M; James, Stephen; Gurcan, Metin N

2012-01-01

The conduct of clinical and translational research regularly involves the use of a variety of heterogeneous and large-scale data resources. Scalable methods for the integrative analysis of such resources, particularly when attempting to leverage computable domain knowledge in order to generate actionable hypotheses in a high-throughput manner, remain an open area of research. In this report, we describe both a generalizable design pattern for such integrative knowledge-anchored hypothesis discovery operations and our experience in applying that design pattern in the experimental context of a set of driving research questions related to the publicly available Osteoarthritis Initiative data repository. We believe that this ‘test bed’ project and the lessons learned during its execution are both generalizable and representative of common clinical and translational research paradigms. PMID:22647689

Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery

PubMed Central

Huo, Zhiguang; Tseng, George

2017-01-01

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K-means (is-K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is-K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency. PMID:28959370

Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery.

PubMed

Huo, Zhiguang; Tseng, George

2017-06-01

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K -means (is- K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is- K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.

How can knowledge discovery methods uncover spatio-temporal patterns in environmental data?

NASA Astrophysics Data System (ADS)

Wachowicz, Monica

2000-04-01

This paper proposes the integration of KDD, GVis and STDB as a long-term strategy, which will allow users to apply knowledge discovery methods for uncovering spatio-temporal patterns in environmental data. The main goal is to combine innovative techniques and associated tools for exploring very large environmental data sets in order to arrive at valid, novel, potentially useful, and ultimately understandable spatio-temporal patterns. The GeoInsight approach is described using the principles and key developments in the research domains of KDD, GVis, and STDB. The GeoInsight approach aims at the integration of these research domains in order to provide tools for performing information retrieval, exploration, analysis, and visualization. The result is a knowledge-based design, which involves visual thinking (perceptual-cognitive process) and automated information processing (computer-analytical process).