Discovering Molecular Pathways from Protein Interaction and Gene Expression data (2003)by E. Segal, H. Wang, and D. Koller
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.
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E. Segal, H. Wang, and D. Koller (2003). "Discovering Molecular Pathways from Protein Interaction and Gene Expression data." Bioinformatics, 19(S1 (Proc ISMB)).
Winner of the ISMB Best Student Paper Award.
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Bibtex citation
@article{Segal+al:ISMB03b,
title = {Discovering Molecular Pathways from Protein Interaction and Gene Expression data},
author = {E. Segal and H. Wang and D. Koller},
journal = {Bioinformatics},
volume = {19},
number = {S1 (Proc ISMB)},
Note = {Winner of the ISMB Best Student Paper Award},
year = 2003,
}
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