Using Bayesian networks to analyze expression data

DNA hybridization arrays simultaneously measure the expression level for th ousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in computational biology is to un cover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework fo r discovering interactions between genes based on multiple expression measu rements. This framework builds on the use of Bayesian networks for represen ting statistical dependencies. A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of c onditional independence between variables. Such models are attractive for t heir ability to describe complex stochastic processes and because they prov ide a clear methodology for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes, We then describe a method for recovering gene interactions from microarray dat a using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al, (199 8).