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• W2134425821 abstract "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. Availability: PHOSFER is available via a web interface at http://saphire.usask.ca. Contact: brett.trost@usask.ca Supplementary information: Supplementary data are available at Bioinformatics online." @default.
• W2134425821 created "2016-06-24" @default.
• W2134425821 creator A5034538230 @default.
• W2134425821 creator A5055263630 @default.
• W2134425821 date "2013-01-22" @default.
• W2134425821 modified "2023-09-27" @default.
• W2134425821 title "Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights" @default.
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• W2134425821 doi "https://doi.org/10.1093/bioinformatics/btt031" @default.
• W2134425821 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23341503" @default.
• W2134425821 hasPublicationYear "2013" @default.
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