  "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.
