SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction.

Ma, Mingyuan; Na, Sen; Zhang, Xiaolu; Chen, Congzhou; Xu, Jin

Ma, Mingyuan; Na, Sen; Zhang, Xiaolu; Chen, Congzhou; Xu, Jin.

Afiliação

Ma M; Key Laboratory of High Confidence Software Technologies of Ministry of Education, School of Computer Science, Peking University, Beijing, China.
Na S; International Computer Science Institute and Department of Statistics, University of California, Berkeley, Berkeley CA, USA.
Zhang X; Department of Information Systems, City University of Hong Kong, Hong Kong, China.
Chen C; Key Laboratory of High Confidence Software Technologies of Ministry of Education, School of Computer Science, Peking University, Beijing, China.
Xu J; Key Laboratory of High Confidence Software Technologies of Ministry of Education, School of Computer Science, Peking University, Beijing, China.

Brief Bioinform ; 23(5)2022 09 20.

Article em En | MEDLINE | ID: mdl-36037084

Assuntos

Neoplasias do Colo; MicroRNAs; Algoritmos; Neoplasias do Colo/genética; Biologia Computacional/métodos; Humanos; MicroRNAs/genética; Redes Neurais de Computação

Palavras-chave

attention mechanism; graph autoencoder; miRNAdisease associations prediction; self-feature embedding

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias do Colo / MicroRNAs Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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