Nucleic Acids Research Advance Access published online on January 30, 2008
Nucleic Acids Research, doi:10.1093/nar/gkm848
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Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection
1Department of Neuroscience, 3Department of Computer Science and Engineering and 4Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA and 2Department of Biology and Biochemistry, University of Houston, Houston, TX 77204, USA
*To whom correspondence should be addressed. Tel: +1 612 626 3481; Fax: +1 612 626 5009; Email: toli{at}biocompute.umn.edu
Received May 15, 2007. Revised August 28, 2007. Accepted September 26, 2007.
Meta-predictors make predictions by organizing and processing the predictions produced by several other predictors in a defined problem domain. A proficient meta-predictor not only offers better predicting performance than the individual predictors from which it is constructed, but it also relieves experimentally researchers from making difficult judgments when faced with conflicting results made by multiple prediction programs. As increasing numbers of predicting programs are being developed in a large number of fields of life sciences, there is an urgent need for effective meta-prediction strategies to be investigated. We compiled four unbiased phosphorylation site datasets, each for one of the four major serine/threonine (S/T) protein kinase families—CDK, CK2, PKA and PKC. Using these datasets, we examined several meta-predicting strategies with 15 phosphorylation site predictors from six predicting programs: GPS, KinasePhos, NetPhosK, PPSP, PredPhospho and Scansite. Meta-predictors constructed with a generalized weighted voting meta-predicting strategy with parameters determined by restricted grid search possess the best performance, exceeding that of all individual predictors in predicting phosphorylation sites of all four kinase families. Our results demonstrate a useful decision-making tool for analysing the predictions of the various S/T phosphorylation site predictors. An implementation of these meta-predictors is available on the web at: http://MetaPred.umn.edu/MetaPredPS/.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
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