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has been cited by the following article:

• TITLE: Improved Protein Phosphorylation Site Prediction by a New Combination of Feature Set and Feature Selection

  AUTHORS: Favorisen Rosyking Lumbanraja, Ngoc Giang Nguyen, Dau Phan, Mohammad Reza Faisal, Bahriddin Abapihi, Bedy Purnama, Mera Kartika Delimayanti, Mamoru Kubo, Kenji Satou

  KEYWORDS: Protein Phosphorylation, Phosphorylation Site Prediction, Sequence Feature, Feature Selection with Grid Search

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.11 No.6, June 29, 2018

  ABSTRACT: Phosphorylation of protein is an important post-translational modification that enables activation of various enzymes and receptors included in signaling pathways. To reduce the cost of identifying phosphorylation site by laborious experiments, computational prediction of it has been actively studied. In this study, by adopting a new set of features and applying feature selection by Random Forest with grid search before training by Support Vector Machine, our method achieved better or comparable performance of phosphorylation site prediction for two different data sets.