pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
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- 作者：Jianhua Jia<sup>asup><sup> ; sup><sup>bsup><sup> ; sup><sup>csup><sup> ; sup><sup>jjia@gordonlifescience.org" class="auth_mail" title="E-mail the corresponding authorsup> ; Zi Liu<sup>asup><sup> ; sup><sup>liuzi189836@163.com" class="auth_mail" title="E-mail the corresponding authorsup> ; Xuan Xiao<sup>asup><sup> ; sup><sup>csup><sup> ; sup><sup>xxiao@gordonlifescience.org" class="auth_mail" title="E-mail the corresponding authorsup> ; Bingxiang Liu<sup>asup><sup> ; sup><sup>lbx1966@163.com" class="auth_mail" title="E-mail the corresponding authorsup> ; Kuo-Chen Chou<sup>csup><sup> ; sup><sup>dsup><sup> ; sup><sup>kcchou@gordonlifescience.org" class="auth_mail" title="E-mail the corresponding authorsup>
- 关键词：Lysine succinylation ; Sequence-coupling model ; General PseAAC ; Random downsampling ; Ensemble random forest ; pSuc-Lys web-server
- 刊名：Journal of Theoretical Biology
- 出版年：2016
- 出版时间：7 April 2016
- 年：2016
- 卷：394
- 期：Complete
- 页码：223-230
- 全文大小：1125 K
文摘
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Succinylation plays an important role in regulating various biological processes.
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A novel ensemble classifier has been developed to predict protein succinylation sites.
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It was formed by fusing a series of individual random forest classifiers via a voting system.
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A user-friendly web-server has been established.