Lumbanraja, Favorisen R and Nguyen, Ngoc G and Phan, Dau and Faisal, Mohammad Reza and Abipihi, Bahriddin and Purnama, Bedy and Delimayanti, Mera K and Kubo, Mamoru and Satou, Kenji (2018) Improved Protein Phosphorylation Site Prediction by a New Combination of Feature Set and Feature Selection. Journal of Biomedical Science and Engineering, 11 (6). pp. 144-157. ISSN 1937-6871

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer
Depositing User: Favorisen R Lumbanraja
Date Deposited: 16 Nov 2018 17:22
Last Modified: 16 Nov 2018 17:22
URI: http://repository.lppm.unila.ac.id/id/eprint/9593

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