Predicting cancer involvement of genes from heterogeneous data

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• dc.contributor.author Aragüés Peleato, Ramónca
• dc.contributor.author Sander, Chrisca
• dc.contributor.author Oliva Miguel, Baldomeroca
• dc.date.accessioned 2012-05-09T08:42:54Z
• dc.date.available 2012-05-09T08:42:54Z
• dc.date.issued 2008ca
• dc.description.abstract Background: Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data. Results: We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature. Conclusion: /nOur approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.
• dc.description.sponsorship RA is supported by a grant from the Spanish Ministerio de Ciencia y Tecnología (MCyT, BIO2002-03609). The work has been supported by grants from the Spanish Ministerio de Educación y Ciencia (MEC, BIO02005-00533) and from the Spanish Ministerio de Ciencia y Tecnologia (PROFIT PSE-010000-2007-1 and FIT-350300-2006-40/41/42)
• dc.format.mimetype application/pdfca
• dc.identifier.citation Aragüés R, Sander C, Oliva, B. Predicting cancer involvement of genes from heterogeneous data. BMC Bioinformatics. 2008;9:172. DOI: 10.1186/1471-2105-9-172ca
• dc.identifier.doi http://dx.doi.org/10.1186/1471-2105-9-172
• dc.identifier.issn 1471-2105ca
• dc.identifier.uri http://hdl.handle.net/10230/16431
• dc.language.iso engca
• dc.publisher BioMed Centralca
• dc.relation.ispartof BMC Bioinformatics. 2008;9:172
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PN/BIO2002-03609
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/BIO2005-00533
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/PSE2007-010000-1
• dc.rights © 2008 Aragüés et al. Creative Commons Attribution Licenseca
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/2.0/
• dc.subject.other Càncer -- Aspectes genètics
• dc.subject.other Interaccions proteïna-proteïna
• dc.title Predicting cancer involvement of genes from heterogeneous dataca
• dc.type info:eu-repo/semantics/articleca
• dc.type.version info:eu-repo/semantics/publishedVersion