Improving the prediction of DNA-protein binding by integrating multi-scale dense convolutional network with fault-tolerant coding.

Yin, Yu-Hang; Shen, Long-Chen; Jiang, Yuanhao; Gao, Shang; Song, Jiangning; Yu, Dong-Jun

Yin, Yu-Hang; Shen, Long-Chen; Jiang, Yuanhao; Gao, Shang; Song, Jiangning; Yu, Dong-Jun.

Afiliação

Yin YH; School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212100, PR China.
Shen LC; School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, PR China.
Jiang Y; School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212100, PR China.
Gao S; School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212100, PR China. Electronic address: gao_shang@just.edu.cn.
Song J; Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia; Monash Data Futures Institute, Monash University, Melbourne, VIC, 3800, Australia. Electronic address: jiangning.song@monash.edu.
Yu DJ; School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, PR China. Electronic address: njyudj@njust.edu.cn.

Anal Biochem ; 656: 114878, 2022 11 01.

Article em En | MEDLINE | ID: mdl-36049552

Assuntos

Redes Neurais de Computação; Software; DNA; Proteínas de Ligação a DNA; Ligação Proteica

Palavras-chave

DNA-Protein binding; Dense connectional network; Fault-tolerant coding; Multi-scale convolution; Sequence analysis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Redes Neurais de Computação Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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