In this paper, we propose a new framework for discovering interactions between genes 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. Such models are attractive for their ability to describe complex stochastic processes, and since they provide clear methodologies 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 data using tools for learning Bayesian networks. Finally,
we apply this method to the S.~cerevisiae cell-cycle measurements
of [Spellman et al, Mol. Bio. Cell, 1999].
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