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dc.contributor.authorKpogbezan, Gino Ben
dc.contributor.authorvan, der Vaart Aad Wen
dc.contributor.authorvan, Wieringen Wessel Nen
dc.contributor.authorLeday, Gwenael GR Ledayen
dc.contributor.authorvan, de Wiel Mark Aen
dc.date.accessioned2017-03-02T10:34:22Z
dc.date.available2017-03-02T10:34:22Z
dc.date.issued2017-09en
dc.identifier.issn0323-3847
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/262824
dc.description.abstractReconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure which automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for pos- terior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In par- ticular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data.
dc.description.sponsorshipThe research leading to these results has received funding from the European Research Council under ERC Grant Agreement 320637.
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.language.isoenen
dc.publisherWiley
dc.titleAn empirical Bayes approach to network recovery using external knowledge.en
dc.typeArticle
prism.endingPage947
prism.publicationDate2017en
prism.publicationNameBiometrical journal. Biometrische Zeitschriften
prism.startingPage932
prism.volume59en
dc.identifier.doi10.17863/CAM.8111
dcterms.dateAccepted2016-12-04en
rioxxterms.versionofrecord10.1002/bimj.201600090en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-09en
dc.identifier.eissn1521-4036
rioxxterms.typeJournal Article/Reviewen
cam.orpheus.successThu Jan 30 12:54:01 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2018-09-30


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