Identifying protein interaction subnetworks by a bagging Markov random field-based method

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dc.contributor.authorChen, Lien
dc.contributor.authorXuan, Jianhuaen
dc.contributor.authorRiggins, Rebecca B.en
dc.contributor.authorWang, Yueen
dc.contributor.authorClark, Robert L.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2013-09-23T18:23:36Zen
dc.date.available2013-09-23T18:23:36Zen
dc.date.issued2013en
dc.description.abstractIdentification of differentially expressed subnetworks from protein-protein interaction (PPI) networks has become increasingly important to our global understanding of the molecular mechanisms that drive cancer. Several methods have been proposed for PPI subnetwork identification, but the dependency among network member genes is not explicitly considered, leaving many important hub genes largely unidentified. We present a new method, based on a bagging Markov random field (BMRF) framework, to improve subnetwork identification for mechanistic studies of breast cancer. The method follows a maximum a posteriori principle to form a novel network score that explicitly considers pairwise gene interactions in PPI networks, and it searches for subnetworks with maximal network scores. To improve their robustness across data sets, a bagging scheme based on bootstrapping samples is implemented to statistically select high confidence subnetworks. We first compared the BMRF-based method with existing methods on simulation data to demonstrate its improved performance. We then applied our method to breast cancer data to identify PPI subnetworks associated with breast cancer progression and/or tamoxifen resistance. The experimental results show that not only an improved prediction performance can be achieved by the BMRF approach when tested on independent data sets, but biologically meaningful subnetworks can also be revealed that are relevant to breast cancer and tamoxifen resistance.en
dc.description.sponsorshipFunding for open access charge: National Institutes of Health [CA149653, CA139246 to J.X., CA149147, HHSN2612200800001E to R.C. and NS29525-18A to Y.W.]; Department of Defense [BC030280 to R.C.].en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationL. Chen, J. Xuan, R. B. Riggins, Y. Wang and R. Clarke, “Identifying protein interaction subnetworks by a bagging Markov random field-based method,” Nucleic Acids Res., 41(2):e42, 2013.en
dc.identifier.doihttps://doi.org/10.1093/nar/gks951en
dc.identifier.issn1362-4962en
dc.identifier.urihttp://hdl.handle.net/10919/23827en
dc.language.isoenen
dc.publisherNucleic Acids Researchen
dc.rightsCreative Commons Attribution-NonCommercial 3.0 Unporteden
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/en
dc.subjectIntegrative network analysisen
dc.subjectProtein-protein interactionsen
dc.subjectMarkov random fielden
dc.subjectComputational biologyen
dc.subjectStatistical learningen
dc.subjectBreast canceren
dc.titleIdentifying protein interaction subnetworks by a bagging Markov random field-based methoden
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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