KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations.

Wu, Jinyang; Ning, Zhiwei; Ding, Yidong; Wang, Ying; Peng, Qinke; Fu, Laiyi

Wu, Jinyang; Ning, Zhiwei; Ding, Yidong; Wang, Ying; Peng, Qinke; Fu, Laiyi.

Afiliação

Wu J; School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China.
Ning Z; School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China.
Ding Y; School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China.
Wang Y; School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China.
Peng Q; School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China.
Fu L; School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China.

Brief Bioinform ; 24(5)2023 09 20.

Article em En | MEDLINE | ID: mdl-37587836

Assuntos

RNA Circular; RNA Longo não Codificante; Humanos; Reconhecimento Automatizado de Padrão; Aprendizagem; Bases de Dados Factuais; Bases de Conhecimento; Biologia Computacional

Palavras-chave

CircRNA-disease associations; Heterogeneous non-coding RNA database; Knowledge graph; Transformer; Web-based visualization

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante / RNA Circular Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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