HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network.

Yang, Yuning; Hou, Zilong; Wang, Yansong; Ma, Hongli; Sun, Pingping; Ma, Zhiqiang; Wong, Ka-Chun; Li, Xiangtao

Yang, Yuning; Hou, Zilong; Wang, Yansong; Ma, Hongli; Sun, Pingping; Ma, Zhiqiang; Wong, Ka-Chun; Li, Xiangtao.

Afiliação

Yang Y; School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China.
Hou Z; School of Artificial Intelligence, Jilin University, Changchun, Jilin, China.
Wang Y; School of Artificial Intelligence, Jilin University, Changchun, Jilin, China.
Ma H; School of Mathematics, Shandong University, Jinan, Shandong, China.
Sun P; School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China.
Ma Z; School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China.
Wong KC; School of Computer Science, City University of Hong Kong, Hong Kong SAR.
Li X; School of Artificial Intelligence, Jilin University, Changchun, Jilin, China.

Brief Bioinform ; 23(2)2022 03 10.

Article em En | MEDLINE | ID: mdl-35189638

Assuntos

Sequenciamento de Cromatina por Imunoprecipitação; RNA Circular; Sítios de Ligação; RNA/genética; RNA/metabolismo; Proteínas de Ligação a RNA/genética; Proteínas de Ligação a RNA/metabolismo

Palavras-chave

circRNA-RBP binding event identification; deep learning; deep temporal convolutional network; natural language processing

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sequenciamento de Cromatina por Imunoprecipitação / RNA Circular Tipo de estudo: Diagnostic_studies 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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