Predicting cancer involvement of genes from heterogeneous data

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Aragüés R, Sander C, Oliva, B. Predicting cancer involvement of genes from heterogeneous data. BMC Bioinformatics. 2008; 9: 172. DOI 10.1186/1471-2105-9-172
http://hdl.handle.net/10230/16431
To cite or link this document: http://hdl.handle.net/10230/16431
dc.contributor.author Aragüés Peleato, Ramón
dc.contributor.author Sander, Chris
dc.contributor.author Oliva, Baldomero
dc.date.accessioned 2012-05-09T08:42:54Z
dc.date.available 2012-05-09T08:42:54Z
dc.date.issued 2008
dc.identifier.citation Aragüés R, Sander C, Oliva, B. Predicting cancer involvement of genes from heterogeneous data. BMC Bioinformatics. 2008; 9: 172. DOI 10.1186/1471-2105-9-172
dc.identifier.issn 1471-2105
dc.identifier.uri http://hdl.handle.net/10230/16431
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: Our 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.language.iso eng
dc.publisher BioMed Central
dc.relation.ispartof BMC Bioinformatics. 2008; 9: 172
dc.rights (c) 2008 Aragüés et al. Creative Commons Attribution License
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 data
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1186/1471-2105-9-172
dc.rights.accessRights info:eu-repo/semantics/openAcces
dc.type.version info:eu-repo/semantics/publishedVersion


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