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

@article{Trost2013ComputationalPS,
  title={Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights},
  author={Brett Trost and Anthony J. Kusalik},
  journal={Bioinformatics},
  year={2013},
  volume={29 6},
  pages={
          686-94
        }
}
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… 
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RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families and compares favorably to other popular mammalian phosphosite prediction methods.
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An improved version of this method, termed RF-Phos 1.1, that employs additional sequence-driven features to identify putative sites of phosphorylation across many protein families performs comparably to or better than other existing phosphosite prediction methods, such as PhosphoSVM, GPS2.1 and Musite.
SKIPHOS: non-kinase specific phosphorylation site prediction with random forests and amino acid skip-gram embeddings
TLDR
This study introduces a non-kinase specific phosphorylation site prediction model based on random forests on top of a continuous distributed representation of amino acids that is compared to three recent methods including PhosphoSVM, iPhos-PreEn and RFPhos.
Phosphorylation site prediction
Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest
TLDR
This work conducts a systematic and hierarchy-specific investigation of protein phosphorylation site prediction in which protein kinases are clustered into hierarchical structures with four levels including kinase, subfamily, family and group and demonstrates that the proposed method remarkably outperforms existing phosphorylated prediction methods at all hierarchical levels.
PhospredRF: Prediction of protein phosphorylation sites using a consensus of random forest classifiers
TLDR
This research work has used machine learning based approaches to predict the position where phosphorylation has occurred and this system could attain the best performance for a set of 22 non-trivial proteins.
Application of Machine Learning Techniques to Predict Protein Phosphorylation Sites
TLDR
A comparison with the predictive performance of PhosphoSVM and Musite reveals that the prediction performance of the proposed method is better, and it has the advantages of simplicity, practicality and low time complexity in classification.
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