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

• 作者: Bahriddin Abapihi Bedy Purnama Dau Phan Favorisen Rosyking Lumbanraja Kenji Satou Mamoru Kubo Mera Kartika Delimayanti Mohammad Reza Faisal Ngoc Giang Nguyen
• 来源期刊: 生物医学工程(英文)
• 学科: 医学
• 关键词: Protein PHOSPHORYLATION PHOSPHORYLATION SITE Prediction Sequence FEATURE FEATURE Selection with GRID SEARCH
• 年，卷（期）: swyxgcyw_2018,（6）
• 页码范围: 144-157
• 页数: 14页
• 分类号: R73

摘要

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.