Reverse-engineering transcriptional modules from gene expression data

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dc.creatorMichoel, Tom
dc.creatorDe Smet, Riet
dc.creatorJoshi, Anagha
dc.creatorMarchal, Kathleen
dc.creatorVan de Peer, Yves
dc.date2009-04-08
dc.date.accessioned2026-07-07T13:01:42Z
dc.date.available2026-07-07T13:01:42Z
dc.description"Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the data set used to learn the models.
dc.description5 pages REVTeX, 4 figures
dc.identifierhttps://arxiv.org/abs/0904.1298
dc.identifierhttp://arxiv.org/abs/0904.1298
dc.identifierAnn. N. Y. Acad. of Sci. 1158, 36 - 43 (2009)
dc.identifierdoi:10.1111/j.1749-6632.2008.03943.x
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/226233
dc.subjectQuantitative Methods
dc.subjectMolecular Networks
dc.titleReverse-engineering transcriptional modules from gene expression data
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