Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers

Proceedings of the National Academy of Sciences of the United States of America

Volumn 116, Issue 28, 2019, Pages 13996-14001

  Ryu, Jae Yong ^a,b   Kim, Hyun Uk ^a   Lee, Sang Yup ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Korea Research Institute of Chemical Technology   (South Korea)

Author keywords

Deep learning; DeepEC; EC number prediction; Enzyme commission number; Metabolism

Indexed keywords

AMINO ACID SEQUENCE; ARTICLE; BINDING SITE; DEEP LEARNING; METABOLISM; MUTATION; PREDICTION; PROTEIN DOMAIN; SOFTWARE; ALGORITHM; BIOLOGY; CHEMISTRY; HUMAN; MACHINE LEARNING; SEQUENCE ANALYSIS;

ENZYME; PROTEIN;

ALGORITHMS; AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; DEEP LEARNING; ENZYMES; HUMANS; MACHINE LEARNING; NEURAL NETWORKS, COMPUTER; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 85068580079     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1821905116     Document Type: Article

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