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Nucleic Acids Research Advance Access published online on August 1, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm562
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods Online

Meta-prediction of protein subcellular localization with reduced voting

Jie Liu1, Shuli Kang1, Chuanning Tang1, Lynda B.M. Ellis2 and Tongbin Li1,*

1Department of Neuroscience and 2Department of Laboratory Medicine and Pathology, University of Minneapolis, MN 55455, 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 June 27, 2007. Accepted July 9, 2007.

Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. We investigated meta-prediction for the four-compartment eukaryotic subcellular localization problem. We compiled an unbiased subcellular localization dataset of 1693 nuclear, cytoplasmic, mitochondrial and extracellular animal proteins from Swiss-Prot 50.2. Using this dataset, we assessed the predicting performance of 12 predictors from eight independent subcellular localization predicting programs: ELSPred, LOCtree, PLOC, Proteome Analyst, PSORT, PSORT II, SubLoc and WoLF PSORT. Gorodkin correlation coefficient (GCC) was one of the performance measures. Proteome Analyst is the best individual subcellular localization predictor tested in this four-compartment prediction problem, with GCC = 0.811. A reduced voting strategy eliminating six of the 12 predictors yields a meta-predictor (RAW-RAG-6) with GCC = 0.856, substantially better than all tested individual subcellular localization predictors (P = 8.2 x 10–6, Fisher's Z-transformation test). The improvement in performance persists when the meta-predictor is tested with data not used in its development. This and similar voting strategies, when properly applied, are expected to produce meta-predictors with outstanding performance in other life sciences problem domains.


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J. Wan, S. Kang, C. Tang, J. Yan, Y. Ren, J. Liu, X. Gao, A. Banerjee, L. B. M. Ellis, and T. Li
Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection
Nucleic Acids Res., March 27, 2008; 36(4): e22 - e22.
[Abstract] [Full Text] [PDF]


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