Protein–protein interaction site prediction through combining local and global features with deep neural networks | Zendy

Min Zeng | Zendy; Fuhao Zhang | Zendy; FangXiang Wu | Zendy; Yaohang Li | Zendy; Jianxin Wang | Zendy; Min Li | Zendy

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Protein–protein interaction site prediction through combining local and global features with deep neural networks

Author(s) -

Min Zeng,

Fuhao Zhang,

FangXiang Wu,

Yaohang Li,

Jianxin Wang,

Min Li

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz699

Subject(s) - computer science , convolutional neural network , sequence (biology) , artificial intelligence , deep learning , artificial neural network , source code , code (set theory) , protein sequencing , sliding window protocol , machine learning , peptide sequence , window (computing) , biology , biochemistry , genetics , set (abstract data type) , gene , programming language , operating system

Protein-protein interactions (PPIs) play important roles in many biological processes. Conventional biological experiments for identifying PPI sites are costly and time-consuming. Thus, many computational approaches have been proposed to predict PPI sites. Existing computational methods usually use local contextual features to predict PPI sites. Actually, global features of protein sequences are critical for PPI site prediction.

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