Predicting novel substrates for enzymes with minimal experimental effort with active learning

Metabolic Engineering

Volumn 44, Issue , 2017, Pages 171-181

  Pertusi, Dante A ^a   Moura, Matthew E ^a   Jeffryes, James G ^a,b   Prabhu, Siddhant ^a   Walters Biggs, Bradley ^a   Tyo, Keith E J ^a  

^a Northwestern University   (United States)

^b ARGONNE NATIONAL LABORATORY   (United States)

Author keywords

Active learning; Enzyme promiscuity; Machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; METABOLISM; STATISTICAL TESTS; SUBSTRATES; SUPPORT VECTOR MACHINES;

ACTIVE LEARNING; COMPUTATIONAL APPROACH; ENZYMATIC REACTION; HIGH PROBABILITY; ITS APPLICATIONS; MAXIMUM ACCURACIES; METABOLIC ENZYMES; METABOLIC PATHWAYS;

ENZYMES;

ENZYME;

ALGORITHM; ARTICLE; DATA BASE; ENZYME METABOLISM; ENZYME SUBSTRATE; ENZYME SUBSTRATE COMPLEX; MACHINE LEARNING; PREDICTIVE VALUE; PRIORITY JOURNAL; PROBABILITY; SUPPORT VECTOR MACHINE; BIOLOGICAL MODEL; ESCHERICHIA COLI; GENETICS; METABOLISM; METABOLOME;

ESCHERICHIA COLI; METABOLOME; MODELS, BIOLOGICAL; SUPPORT VECTOR MACHINE;

EID: 85031768728     PISSN: 10967176     EISSN: 10967184     Source Type: Journal    
DOI: 10.1016/j.ymben.2017.09.016     Document Type: Article

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