Improved Protein Phosphorylation Site Prediction by a New Combination of Feature Set and Feature Selection
| Autor: | Bedy Purnama, Mamoru Kubo, Bahriddin Abapihi, Mera Kartika Delimayanti, Kenji Satou, Ngoc Giang Nguyen, Dau Phan, Mohammad Reza Faisal, Favorisen Rosyking Lumbanraja |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
030102 biochemistry & molecular biology Computer science Feature selection Computational biology Random forest Set (abstract data type) Support vector machine 03 medical and health sciences 030104 developmental biology Hyperparameter optimization Phosphorylation Protein phosphorylation Feature set |
| Zdroj: | Journal of Biomedical Science and Engineering. 11:144-157 |
| ISSN: | 1937-688X 1937-6871 |
| DOI: | 10.4236/jbise.2018.116013 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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