multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction.

Xuan, Ping; Zhang, Xiaowen; Zhang, Yu; Hu, Kaimiao; Nakaguchi, Toshiya; Zhang, Tiangang

Xuan, Ping; Zhang, Xiaowen; Zhang, Yu; Hu, Kaimiao; Nakaguchi, Toshiya; Zhang, Tiangang.

Afiliação

Xuan P; School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
Zhang X; School of Computer Science, Shaanxi Normal University, Xi'an 710062, China.
Zhang Y; School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
Hu K; School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
Nakaguchi T; School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
Zhang T; Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan.

Brief Bioinform ; 23(5)2022 09 20.

Article em En | MEDLINE | ID: mdl-35514190

Assuntos

Algoritmos; Redes Neurais de Computação; Desenvolvimento de Medicamentos; Interações Medicamentosas; Aprendizagem

Palavras-chave

attentions at neighbor-type level and at network level; drugprotein interaction; graph neural network with neighbor node-level attention; multi-type neighbors enhanced global topology; multiple drugprotein heterogenous networks; three-dimensional convolutional neural network

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Redes Neurais de Computação Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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