Journal
BIOINFORMATICS
Volume 19, Issue -, Pages i264-i272Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btg1037
Keywords
probabilistic models; protein interaction; gene expression
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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.
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