Reverse-engineering transcriptional modules from gene expression data.

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 dataset used to learn the models.

journal_name

Ann N Y Acad Sci

authors

Michoel T,De Smet R,Joshi A,Marchal K,Van de Peer Y

doi

10.1111/j.1749-6632.2008.03943.x

subject

Has Abstract

pub_date

2009-03-01 00:00:00

pages

36-43

eissn

0077-8923

issn

1749-6632

pii

NYAS03943

journal_volume

1158

pub_type

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