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.
Department of Plant Systems Biology, VIB, Gent, Belgium. tom.michoel@psb.ugent.be
Tom
T
Michoel
Riet
R
De Smet
Anagha
A
Joshi
Kathleen
K
Marchal
Yves
Y
Van de Peer
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