DSGAT: predicting frequencies of drug side effects by graph attention networks.

Xu, Xianyu; Yue, Ling; Li, Bingchun; Liu, Ying; Wang, Yuan; Zhang, Wenjuan; Wang, Lin

Xu, Xianyu; Yue, Ling; Li, Bingchun; Liu, Ying; Wang, Yuan; Zhang, Wenjuan; Wang, Lin.

Afiliação

Xu X; College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China.
Yue L; College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China.
Li B; College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China.
Liu Y; College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China.
Wang Y; College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China.
Zhang W; College of General Education, Tianjin Foreign Studies University, Tianjin, China.
Wang L; College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China.

Brief Bioinform ; 23(2)2022 03 10.

Article em En | MEDLINE | ID: mdl-35043189

Assuntos

Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos; Benchmarking; Humanos; Software

Palavras-chave

chemical structure; cold start; deep learning; graph attention network; side effect frequency

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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