Journal Article

Predicting homologous signaling pathways using machine learning

Babak Bostan, Russell Greiner, Duane Szafron and Paul Lu

in Bioinformatics

Volume 25, issue 22, pages 2913-2920
Published in print November 2009 | ISSN: 1367-4803
Published online September 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp532
Predicting homologous signaling pathways using machine learning

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Motivation: In general, each cell signaling pathway involves many proteins, each with one or more specific roles. As they are essential components of cell activity, it is important to understand how these proteins work—and in particular, to determine which of the species' proteins participate in each role. Experimentally determining this mapping of proteins to roles is difficult and time consuming. Fortunately, many pathways are similar across species, so we may be able to use known pathway information of one species to understand the corresponding pathway of another.

Results: We present an automatic approach, Predict Signaling Pathway (PSP), which uses the signaling pathways in well-studied species to predict the roles of proteins in less-studied species. We use a machine learning approach to create a predictor that achieves a generalization F-measure of 78.2% when applied to 11 different pathways across 14 different species. We also show our approach is very effective in predicting the pathways that have not yet been experimentally studied completely.

Availability: The list of predicted proteins for all pathways over all considered species is available at http://www.cs.ualberta.ca/~bioinfo/signaling.

Contact: bioinfo@cs.ualberta.ca; duane@cs.ualberta.ca

Journal Article.  6603 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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