In this paper, we describe an approach for identifying pathways from gene
expression and protein interaction data. Our approach is based on the
assumption that many pathways exhibit two properties: their genes exhibit a
similar gene expression profile, and the protein products of the genes often
interact. Our approach is based on a unified probabilistic model, which is
learned from the data using the EM algorithm. We present results on two
Saccharomyces cerevisiae gene expression data sets, combined with a binary
protein interaction data set. Our results show that our approach is much
more successful than other approaches at discovering both coherent
functional groups and entire protein complexes.