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dc.contributor.authorVenkatesan, Aravind
dc.contributor.authorTripathi, Sushil
dc.contributor.authorSanz de Galdeano, Alejandro
dc.contributor.authorBlondé, Ward
dc.contributor.authorLægreid, Astrid
dc.contributor.authorMironov, Vladimir
dc.contributor.authorKuiper, Martin
dc.identifier.citationBMC Bioinformatics. 2014 Dec 10;15:386.es_ES
dc.description.abstractBACKGROUND: Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. RESULTS: We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. CONCLUSIONS: Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.es_ES
dc.description.sponsorshipTechnical support was provided by the High-Performance Computing team at the Norwegian University of Science and Technology. The work was supported by The Norwegian Cancer Society.es_ES
dc.relation.isversionofPublisher's versiones_ES
dc.subject.meshComputational Biology es_ES
dc.subject.meshGenomics es_ES
dc.subject.meshHumans es_ES
dc.subject.meshKnowledge Bases es_ES
dc.subject.meshProtein Interaction Maps es_ES
dc.subject.meshSemantics es_ES
dc.subject.meshGene Expression Regulation es_ES
dc.subject.meshGene Regulatory Networks es_ES
dc.subject.meshModels, Biologicales_ES
dc.subject.meshSignal Transduction es_ES
dc.titleFinding gene regulatory network candidates using the gene expression knowledge basees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.contributor.funderNorwegian University of Life Sciences
dc.identifier.journalBMC bioinformaticses_ES
dc.repisalud.centroISCIII::Escuela Nacional de Sanidades_ES

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Atribución 4.0 Internacional
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