Machine learning framework for assessment of microbial factory performance

PLoS ONE

Volumn 14, Issue 1, 2019, Pages

  Oyetunde, Tolutola ^a   Liu, Di ^a,f   Martin, Hector Garcia ^b,c,d,e   Tang, Yinjie J ^a  

^a WASHINGTON UNIVERSITY   (United States)

^b JOINT BIOENERGY INSTITUTE   (United States)

^c DOE Agile Biofoundry   (United States)

^d LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^e BCAM BASQUE CENTER FOR APPLIED MATHEMATICS   (Spain)

^f SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOPROCESS; CORRELATION COEFFICIENT; CORRESPONDENCE ANALYSIS; DNA MODIFICATION; ESCHERICHIA COLI; GENOME; HUMAN; MACHINE LEARNING; NONHUMAN; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; RUNNING; SIMULATION; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; ALGORITHM; BIOLOGICAL MODEL; COMPUTER SIMULATION; FACTUAL DATABASE; GENETICS; METABOLIC ENGINEERING; METABOLISM; PROCEDURES; REPRODUCIBILITY;

ALGORITHMS; COMPUTER SIMULATION; DATABASES, FACTUAL; ESCHERICHIA COLI; MACHINE LEARNING; METABOLIC ENGINEERING; MODELS, BIOLOGICAL; REPRODUCIBILITY OF RESULTS;

EID: 85060049392     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0210558     Document Type: Article

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