Discovering molecular pathways from protein interaction and gene expression data.
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.
Segal
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Computer Science Department, Stanford University, Stanford, CA 94305-9010, USA. eran@cs.stanford.edu
Wang
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Koller
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D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics* / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology; D018511:Systems Integration; D014584:User-Computer Interface
D029701:Saccharomyces cerevisiae Proteins