Deep learning methods for protein torsion angle prediction

BMC Bioinformatics

Volumn 18, Issue 1, 2017, Pages

  Li, Haiou ^a   Hou, Jie ^b   Adhikari, Badri ^c   Lyu, Qiang ^a   Cheng, Jianlin ^b  

^a SOOCHOW UNIVERSITY   (China)

^b University of Missouri   (United States)

^c UNIVERSITY OF MISSOURI ST LOUIS   (United States)

Author keywords

Deep learning; Deep recurrent neural network; Protein torsion angle prediction; Restricted Boltzmann machine

Indexed keywords

DEEP NEURAL NETWORKS; DIHEDRAL ANGLE; ERRORS; FORECASTING; LEARNING SYSTEMS; NETWORK ARCHITECTURE; PROTEINS; RECURRENT NEURAL NETWORKS; TORSIONAL STRESS;

FEED-FORWARD ARCHITECTURES; LEARNING ARCHITECTURES; MACHINE LEARNING METHODS; RESTRICTED BOLTZMANN MACHINE; SECONDARY STRUCTURE PREDICTION; STATE-OF-THE-ART METHODS; STATE-OF-THE-ART PERFORMANCE; TORSION ANGLE;

DEEP LEARNING;

PROTEIN;

ARTIFICIAL NEURAL NETWORK; CHEMICAL STRUCTURE; CHEMISTRY; MACHINE LEARNING; PROTEIN SECONDARY STRUCTURE;

MACHINE LEARNING; MOLECULAR STRUCTURE; NEURAL NETWORKS (COMPUTER); PROTEIN STRUCTURE, SECONDARY; PROTEINS;

EID: 85029659778     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-017-1834-2     Document Type: Article

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