dc.contributor.author | Venkatesan, Aravind | |
dc.contributor.author | Tripathi, Sushil | |
dc.contributor.author | Sanz de Galdeano, Alejandro | |
dc.contributor.author | Blondé, Ward | |
dc.contributor.author | Lægreid, Astrid | |
dc.contributor.author | Mironov, Vladimir | |
dc.contributor.author | Kuiper, Martin | |
dc.date.accessioned | 2015-01-06T10:16:56Z | |
dc.date.accessioned | 2016-04-20T11:26:20Z | |
dc.date.available | 2015-01-06T10:16:56Z | |
dc.date.available | 2016-04-20T11:26:20Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | BMC Bioinformatics 2014, 15:386 | nb_NO |
dc.identifier.issn | 1471-2105 | |
dc.identifier.uri | http://hdl.handle.net/11250/2386483 | |
dc.description.abstract | Background: 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.iso | eng | nb_NO |
dc.publisher | BioMed Central | nb_NO |
dc.rights | Navngivelse 3.0 Norge | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/no/ | * |
dc.title | Finding gene regulatory network candidates using the gene expression knowledge base | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.date.updated | 2015-01-06T10:16:56Z | |
dc.source.volume | 15 | nb_NO |
dc.source.journal | BMC Bioinformatics | nb_NO |
dc.identifier.doi | 10.1186/s12859-014-0386-y | |
dc.identifier.cristin | 1184927 | |
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 |