Genome-wide expression profiles of different conditions (e.g. ic mutants and chemical treatments) 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 talk we show how to discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. Our proposed framework for discovering interactions between genes is based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. In this talk we demonstrate methods for inferring 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.
