Multi-channel graph attention autoencoders for disease-related lncRNAs prediction.

Sheng, Nan; Huang, Lan; Wang, Yan; Zhao, Jing; Xuan, Ping; Gao, Ling; Cao, Yangkun

Sheng, Nan; Huang, Lan; Wang, Yan; Zhao, Jing; Xuan, Ping; Gao, Ling; Cao, Yangkun.

Afiliação

Sheng N; Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Huang L; Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Wang Y; Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Zhao J; School of Artificial Intelligence, Jilin University, Changchun 130012, China.
Xuan P; Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus OH 43210, USA.
Gao L; School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
Cao Y; School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.

Brief Bioinform ; 23(2)2022 03 10.

Article em En | MEDLINE | ID: mdl-35108355

Assuntos

MicroRNAs; RNA Longo não Codificante; Algoritmos; Biologia Computacional/métodos; Humanos; MicroRNAs/genética; Redes Neurais de Computação; RNA Longo não Codificante/genética

Palavras-chave

disease-related lncRNAs prediction; graph autoencoders; graph-level attention mechanism; random forest

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: MicroRNAs / RNA Longo não Codificante Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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