DeepQA: Improving the estimation of single protein model quality with deep belief networks

BMC Bioinformatics

Volumn 17, Issue 1, 2016, Pages

  Cao, Renzhi ^a   Bhattacharya, Debswapna ^b   Hou, Jie ^c   Cheng, Jianlin ^c,d  

^a PACIFIC LUTHERAN UNIVERSITY   (United States)

^b WICHITA STATE UNIVERSITY   (United States)

^c University of Missouri   (United States)

^d UNIVERSITY OF MISSOURI   (United States)

Author keywords

Deep belief network; Machine learning; Protein model quality assessment; Protein structure prediction

Indexed keywords

BAYESIAN NETWORKS; COMPUTER OPERATING SYSTEMS; FORECASTING; LEARNING SYSTEMS; PROTEINS;

AB INITIO MODELING; CRITICAL ASSESSMENT OF PROTEIN STRUCTURE PREDICTIONS (CASP); DEEP BELIEF NETWORKS; PROTEIN MODELING; PROTEIN STRUCTURE PREDICTION; PROTEIN TERTIARY STRUCTURE PREDICTION; STATE-OF-THE-ART PERFORMANCE; STRUCTURAL INFORMATION;

DEEP LEARNING;

PROTEIN;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; MACHINE LEARNING; MEASUREMENT ACCURACY; METABOLISM; MOLECULAR MODEL; PROTEIN TERTIARY STRUCTURE; SUPPORT VECTOR MACHINE;

ALGORITHMS; DATA ACCURACY; MACHINE LEARNING; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PROTEIN STRUCTURE, TERTIARY; PROTEINS; SUPPORT VECTOR MACHINE;

EID: 85000843987     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-016-1405-y     Document Type: Article

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