Discovering molecular pathways from protein interaction and gene expression data.

by: E Segal, H Wang, D Koller

Bioinformatics, Vol. 19 Suppl 1 (2003)

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摘要

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.

@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.}, 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}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {bio, expressiondata, gene, interactiondata, learning, network, pathway, protein, structure}, posted-at = {2006-05-28 15:49:04}, priority = {2}, title = {Discovering molecular pathways from protein interaction and gene expression data.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12855469}, volume = {19 Suppl 1}, year = {2003} }

RIS record

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. JF - Bioinformatics SN - 1367-4803 AD - Computer Science Department, Stanford University, Stanford, CA 94305-9010, USA. eran@cs.stanford.edu TI - Discovering molecular pathways from protein interaction and gene expression data. VL - 19 Suppl 1 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/// UR - http://view.ncbi.nlm.nih.gov/pubmed/12855469 ER -

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