MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network.

Dong, Guiying; Zhang, Zi-Chao; Feng, Jianfeng; Zhao, Xing-Ming

Dong, Guiying; Zhang, Zi-Chao; Feng, Jianfeng; Zhao, Xing-Ming.

Afiliação

Dong G; Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China.
Zhang ZC; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
Feng J; Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China.
Zhao XM; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.

Brief Bioinform ; 23(4)2022 07 18.

Article em En | MEDLINE | ID: mdl-35780382

Assuntos

Multimorbidade; Humanos; Fenótipo

Palavras-chave

disease network; graph convolutional network; multimorbidity; population phenotype

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Multimorbidade Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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