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Predicting Homologous Signaling Pathways Using Machine Learning

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 a 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), that 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.

Citation

B. Bostan, R. Greiner, D. Szafron, P. Lu. "Predicting Homologous Signaling Pathways Using Machine Learning". Bioinformatics, September 2009.

Keywords: Signaling Pathway, machine learning, Protein prediction, medical informatics, bioinformatics
Category: In Journal
Web Links: Predicting Homologous Signaling Pathways Using machine Learning

BibTeX

@article{Bostan+al:Bioinformatics09,
  author = {Babak Bostan and Russ Greiner and Duane Szafron and Paul Lu},
  title = {Predicting Homologous Signaling Pathways Using Machine Learning},
  journal = {Bioinformatics},
  year = 2009,
}

Last Updated: January 30, 2016
Submitted by Russ Greiner

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