Published in
American Society for Microbiology, Journal of Clinical Microbiology, 2(57), 2019
DOI: 10.1128/jcm.01260-18
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Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella
Journal article published in 2019 by
Marcus Nguyen,
S. Wesley Long 
,
Patrick F. McDermott,
Randall J. Olsen,
Robert Olson,
Rick L. Stevens,
Gregory H. Tyson ,
Shaohua Zhao,
James J. Davis
,
Patrick F. McDermott,
Randall J. Olsen,
Robert Olson,
Rick L. Stevens,
Gregory H. Tyson ,
Shaohua Zhao,
James J. Davis
This paper is made freely available by the publisher.
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Abstract
Nontyphoidal Salmonella species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies.