MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.

Zhong, Yating; Peng, Yuzhong; Lin, Yanmei; Chen, Dingjia; Zhang, Hao; Zheng, Wen; Chen, Yuanyuan; Wu, Changliang

Zhong, Yating; Peng, Yuzhong; Lin, Yanmei; Chen, Dingjia; Zhang, Hao; Zheng, Wen; Chen, Yuanyuan; Wu, Changliang.

Afiliação

Zhong Y; Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China.
Peng Y; Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China. jedison@163.com.
Lin Y; School of Environment and Life Science, Nanning Normal University, Nanning, 530001, China. ymlin20160714@163.com.
Chen D; Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China.
Zhang H; School of Computer Science, Fudan University, Shanghai, 200433, China.
Zheng W; School of Computer, Guangdong University of Petrochemical Technology, Maoming, 525000, China.
Chen Y; Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China.
Wu C; Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China.

BMC Med Inform Decis Mak ; 23(1): 82, 2023 05 05.

Article em En | MEDLINE | ID: mdl-37147619

Assuntos

MicroRNAs; Multiômica; Humanos; Algoritmos; MicroRNAs/genética; Redes Neurais de Computação; Metilação de DNA

Palavras-chave

Complex disease classification; Deep learning; Graph Attention Networks; Multi-omics data integration

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: MicroRNAs / Multiômica Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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