Reverse-engineering transcriptional modules from gene expression data release_z5vippl6f5b3tjfmf6u7pl5qha

by Tom Michoel, Riet De Smet, Anagha Joshi, Kathleen Marchal, Yves Van de Peer

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2009  

Abstract

"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.
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Date   2009-04-08
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arXiv  0904.1298v1
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