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. 2013 Mar 15;29(6):686-94.
doi: 10.1093/bioinformatics/btt031. Epub 2013 Jan 22.

Computational Phosphorylation Site Prediction in Plants Using Random Forests and Organism-Specific Instance Weights

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Computational Phosphorylation Site Prediction in Plants Using Random Forests and Organism-Specific Instance Weights

Brett Trost et al. Bioinformatics. .

Abstract

Motivation: Phosphorylation is the most important post-translational modification in eukaryotes. Although many computational phosphorylation site prediction tools exist for mammals, and a few were created specifically for Arabidopsis thaliana, none are currently available for other plants.

Results: In this article, we propose a novel random forest-based method called PHOSFER (PHOsphorylation Site FindER) for applying phosphorylation data from other organisms to enhance the accuracy of predictions in a target organism. As a test case, PHOSFER is applied to phosphorylation sites in soybean, and we show that it more accurately predicts soybean sites than both the existing Arabidopsis-specific predictors, and a simpler machine-learning scheme that uses only known phosphorylation sites and non-phosphorylation sites from soybean. In addition to soybean, PHOSFER will be extended to other organisms in the near future.

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