Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

PLoS Computational Biology

Volumn 13, Issue 1, 2017, Pages

  Wang, Sheng ^a   Sun, Siqi ^a   Li, Zhen ^a   Zhang, Renyu ^a   Xu, Jinbo ^a  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MEMBRANES; CONVOLUTION; DEEP NEURAL NETWORKS; FORECASTING;

FOLDINGS; LEARNING METHODS; LEARNING MODELS; LOW QUALITIES; MEMBRANE PROTEINS; PROTEIN CONTACT MAPS; PROTEIN FUNCTIONS; PROTEINS STRUCTURES; STRUCTURE PREDICTION; ULTRA DEEPS;

PROTEINS;

MEMBRANE PROTEIN; PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; EVOLUTIONARY COUPLING; MEASUREMENT ACCURACY; PREDICTOR VARIABLE; PROTEIN CONTACT MAP; PROTEIN FOLDING; SEQUENCE ALIGNMENT; SEQUENCE HOMOLOGY; STATISTICAL ANALYSIS; STATISTICAL CONCEPTS; ULTRA DEEP LEARNING METHOD; ALGORITHM; BIOLOGY; CHEMISTRY; MACHINE LEARNING; METABOLISM; PROCEDURES; PROTEIN CONFORMATION; PROTEIN DATABASE; SEQUENCE ANALYSIS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); PROTEIN CONFORMATION; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 85011370897     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005324     Document Type: Article

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