Predicting Homologous Signaling Pathways Using Machine Learning
- Babak Bostan, Department of Computing Science
- Russ Greiner, Dept of Computing Science; PI of AICML
- Duane Szafron, UofA CS
- Paul Lu, Department of Computing Science
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