Genome-wide expression profiles of genetic mutants
provide a wide variety of measurements of cellular responses to perturbations.
Typical analysis of such data identifies genes
affected by perturbation and uses clustering to
group genes of similar function.
In this paper we
discover a finer structure of interactions between genes, such as
causality, mediation, activation, and inhibition by
using a Bayesian network framework.
We extend this framework to correctly handle perturbations,
and to identify significant subnetworks of interacting genes.
We apply
this method
to expression data of S.~cerevisiae
mutants and uncover a variety of structured
metabolic, signaling and regulatory pathways.
