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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.date.accessioned2015-01-06T10:16:56Z
dc.date.accessioned2016-04-20T11:26:20Z
dc.date.available2015-01-06T10:16:56Z
dc.date.available2016-04-20T11:26:20Z
dc.date.issued2014
dc.identifier.citationBMC Bioinformatics 2014, 15:386nb_NO
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/11250/2386483
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.nb_NO
dc.language.isoengnb_NO
dc.publisherBioMed Centralnb_NO
dc.rightsNavngivelse 3.0 Norge*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/no/*
dc.titleFinding gene regulatory network candidates using the gene expression knowledge basenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.date.updated2015-01-06T10:16:56Z
dc.source.volume15nb_NO
dc.source.journalBMC Bioinformaticsnb_NO
dc.identifier.doi10.1186/s12859-014-0386-y
dc.identifier.cristin1184927
dc.description.localcode© Venkatesan et al.; licensee BioMed Central. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​4.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.nb_NO


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