Reverse-engineering transcriptional modules from gene expression data

"Module networks" are a framework to learn gene regulatory networks from expression data using aprobabilistic 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 extractthe statistically most significant modules and their regulators. We show that the inferred probabilisticmodels extend beyond the data set used to learn the models.

Michoel, T., De Smet, R., Joshi, A., Marchal, K., Van de Peer, Y. (2009) Reverse-engineering transcriptional modules from gene expression data. Annals of the New York Academy of Sciences 1158, 36-43 .

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