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

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.

Source:

Journal: Journal of Biomedical Science and Engineering
DOI: 10.4236/jbise.2018.116013 (PDF)
Paper Id: 85690 (metadata)

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