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Improved Protein Phosphorylation Site Prediction by a New Combination of Feature Set and Feature Selection
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- 1 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 2 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 3 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 4 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 5 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 6 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 7 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 8 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- 9 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Journal of Biomedical Science and Engineering·Volume 11 (2018)·Pages 144–157·Published 12 June 2018·DOI10.4236/jbise.2018.116013
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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.
KeywordsProtein PhosphorylationPhosphorylation Site PredictionSequence FeatureFeature Selection with Grid Search
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