from Perturbed Expression Profiles
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.
to expression data of S.~cerevisiae
mutants and uncover a variety of structured
metabolic, signaling and regulatory pathways.
We applied our methodology to
Rosetta Inpharmatics Compendium
Dataset ( Hughes et. al. Cell 2000 ).
The annotated results can be interactively viewed using
We stress that no prior biological knowledge was used by
our learning procedure when reconstructing the networks.