Discovering molecular pathways from protein interaction and gene expression data Export

@article{citeulike:610898, 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. Contact: eran@cs.stanford.edu Keywords: probabilistic models, protein interaction, gene expression. 10.1093/bioinformatics/btg1037}, address = {Computer Science Department, Stanford University, Stanford, CA 94305-9010, USA. eran@cs.stanford.edu}, author = {Segal, E. and Wang, H. and Koller, D.}, citeulike-article-id = {610898}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btg1037}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/19/suppl_1/i264}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/12855469}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=12855469}, day = {3}, doi = {10.1093/bioinformatics/btg1037}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {bio, expressiondata, gene, interactiondata, learning, network, pathway, protein, structure}, month = {July}, number = {suppl\_1}, pages = {i264--272}, posted-at = {2006-05-28 15:49:04}, priority = {2}, title = {Discovering molecular pathways from protein interaction and gene expression data}, url = {http://dx.doi.org/10.1093/bioinformatics/btg1037}, volume = {19}, year = {2003} }

TY - JOUR ID - citeulike:610898 L3 - citeulike-article-id:610898 N2 - 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. Contact: eran@cs.stanford.edu Keywords: probabilistic models, protein interaction, gene expression. 10.1093/bioinformatics/btg1037 IS - suppl_1 JF - Bioinformatics SN - 1367-4803 AD - Computer Science Department, Stanford University, Stanford, CA 94305-9010, USA. eran@cs.stanford.edu EP - 272 TI - Discovering molecular pathways from protein interaction and gene expression data VL - 19 SP - i264 KW - bio KW - expressiondata KW - gene KW - interactiondata KW - learning KW - network KW - pathway KW - protein KW - structure AU - Segal, E AU - Wang, H AU - Koller, D PY - 2003/07/03/ UR - http://dx.doi.org/10.1093/bioinformatics/btg1037 ER -