Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights.

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2013
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Abstract
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.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.
Reference Key
trost2013computationalbioinformatics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Trost, Brett;Kusalik, Anthony;
Journal Bioinformatics
Year 2013
DOI
10.1093/bioinformatics/btt031
URL
Keywords
Cytotoxicity Proteomics systems biology metals risk assessment chemical mixtures chemical interactions dose response mixture toxicity

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