DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.

Chu, Yanyi; Kaushik, Aman Chandra; Wang, Xiangeng; Wang, Wei; Zhang, Yufang; Shan, Xiaoqi; Salahub, Dennis Russell; Xiong, Yi; Wei, Dong-Qing

Chu, Yanyi; Kaushik, Aman Chandra; Wang, Xiangeng; Wang, Wei; Zhang, Yufang; Shan, Xiaoqi; Salahub, Dennis Russell; Xiong, Yi; Wei, Dong-Qing.

Afiliação

Chu Y; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University.
Kaushik AC; School of Medicine, Jiangnan University, Wuxi, China.
Wang X; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University.
Wang W; Mathematical Sciences, Shanghai Jiao Tong University.
Zhang Y; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University.
Shan X; School of Life Sciences and Biotechnology.
Salahub DR; Department of Chemistry, University of Calgary, Fellow Royal Society of Canada.
Xiong Y; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University.
Wei DQ; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University.

Brief Bioinform ; 22(1): 451-462, 2021 01 18.

Article em En | MEDLINE | ID: mdl-31885041

Assuntos

Desenvolvimento de Medicamentos/métodos; Proteômica/métodos; Software; Humanos; Simulação de Acoplamento Molecular/métodos; Análise de Sequência de Proteína/métodos

Palavras-chave

cascade deep forest; drug-target interaction; ensemble learning; machine learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Proteômica / Desenvolvimento de Medicamentos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de publicação: Reino Unido

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