title: Discovering molecular pathways from protein interaction and gene expression data.
abstract: 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.
authors.affiliation: Computer Science Department, Stanford University, Stanford, CA 94305-9010, USA. eran@cs.stanford.edu
authors.forename: E
authors.initials: E
authors.lastname: Segal
authors.forename: H
authors.initials: H
authors.lastname: Wang
authors.forename: D
authors.initials: D
authors.lastname: Koller
mesh_terms: 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
chemical_list: D029701:Saccharomyces cerevisiae Proteins