title: Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights.
abstract: BACKGROUND
  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.
authors: Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada. brett.trost@usask.ca
authors: Brett
authors: B
authors: Trost
authors: Anthony
authors: A
authors: Kusalik
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D002417:Cattle; D019295:Computational Biology / Q000379:methods; D006801:Humans; D051379:Mice; D010766:Phosphorylation; D010940:Plant Proteins / Q000737:chemistry / Q000378:metabolism*; D011499:Protein Processing, Post-Translational; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software; D013025:Glycine max / Q000378:metabolism
chemical_list: D010940:Plant Proteins