ABSTRACT
DNA microarray experiments simultaneously measure the expression levels of thousands of genes. An important problem in computational biology is to discover, from such measurements, gene interaction networks and key biological features of cellular systems. One of the most used approaches for this problem is to learn a Bayesian network (Pearl, 1988; Cowell, 1999) from the gene expression data. The Bayesian network is a powerful knowledge representation and reasoning tool under conditions of uncertainty that is typical of real-life applications. The Bayesian network, as illustrated by Figure 11.1, is a directed acyclic graph (DAG) in which the nodes represent the variables in the domain and the edges correspond to direct probabilistic dependencies between them. In a Bayesian network of gene interaction, the nodes represent different genes.
