Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach.

Hu, Lun; Li, Zhenfeng; Tang, Zehai; Zhao, Cheng; Zhou, Xi; Hu, Pengwei

Hu, Lun; Li, Zhenfeng; Tang, Zehai; Zhao, Cheng; Zhou, Xi; Hu, Pengwei.

Afiliação

Hu L; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.
Li Z; School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China.
Tang Z; School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China.
Zhao C; School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China.
Zhou X; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China. zhouxi@ms.xjb.ac.cn.
Hu P; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China. hupengwei@hotmail.com.

BMC Bioinformatics ; 23(1): 447, 2022 Oct 27.

Article em En | MEDLINE | ID: mdl-36303135

Assuntos

Protease de HIV; HIV-1; Algoritmos; Protease de HIV/química; HIV-1/enzimologia; Aprendizado de Máquina; Especificidade por Substrato

Palavras-chave

Asymmetric bagging; Biased SVM; Cleavage sites prediction; Ensemble learning; HIV-1 protease

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Protease de HIV / HIV-1 Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido

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