Reverse-engineering transcriptional modules from gene expression data.
"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.
Michoel
Tom
T
Department of Plant Systems Biology, VIB, Gent, Belgium. tom.michoel@psb.ugent.be
De Smet
Riet
R
Joshi
Anagha
A
Marchal
Kathleen
K
Van de Peer
Yves
Y
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D015870:Gene Expression; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D012984:Software