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

Favorisen Rosyking Lumbanraja; Ngoc Giang Nguyen; Dau Phan; Mohammad Reza Faisal; Bahriddin Abapihi; Bedy Purnama; Mera Kartika Delimayanti; Mamoru Kubo; Kenji Satou

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

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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.

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《生物医学工程（英文）》 |2018年第6期|144-157|共14页
作者
Favorisen Rosyking Lumbanraja; Ngoc Giang Nguyen; Dau Phan; Mohammad Reza Faisal; Bahriddin Abapihi; Bedy Purnama; Mera Kartika Delimayanti; Mamoru Kubo; Kenji Satou;
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作者单位

Graduate School of Natural Science and Technology;

Kanazawa University;

Kanazawa;

Japan;

Institute of Science and Engineering;

Kanazawa University;

Kanazawa;

Japan;

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原文格式 PDF
正文语种 chi
中图分类肿瘤学;
关键词
Protein; Phosphorylation; Phosphorylation; Site; Prediction; Sequence; Feature; Feature; Selection; with; Grid; Search;

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

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