PrAS: Prediction of amidation sites using multiple feature extraction

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• 作者：Tong Wang^a ; ^{tongwang1992@mail.nankai.edu.cn} ; Wei Zheng^a ; ^{jlspzw139@sina.com} ; Qiqige Wuyun^a ; ^{wuyunqqg@163.com} ; Zhenfeng Wu^a ; ^{wu_zhenfeng@mail.nankai.edu.cn} ; Jishou Ruan^a ; ^b ; ^{jsruan@nankai.edu.cn} ; Gang Hu^a ; ^{huggs@nankai.edu.cn} ; Jianzhao Gao^a ; ^{gaojz@nankai.edu.cn}
• 关键词：Posttranslational modification (PTM) ; Amidation sites ; Support vector machine (SVM) ; Positive contribution feature selection
• 刊名：Computational Biology and Chemistry
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：66
• 期：Complete
• 页码：57-62
• 全文大小：547 K
• 卷排序：66

文摘

Propose the first user-friendly tool, PrAS for predicting protein amidation sites. PrAS achieved AUC of 0.96, MCC of 0.76 on the independent test set. Propose an efficient feature selection approach, positive contribution feature selection (PCFS).