Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella
Abstract
Nontyphoidal Salmonella species are the leading bacterial cause of food-borne 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. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, were used to generate XGBoost-based machine learning models for predicting minimum inhibitory concentrations (MICs) for 15 antibiotics. The MIC prediction models have an overall average accuracy of 95% within ± 1 two-fold dilution step (confidence interval of 95-95%), an average very major error rate of 2.7% (confidence interval of 2.4-3.0%) and an average major error rate of 0.1% (confidence interval of 0.1-0.2%). The model predicts MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for training sets, we show that highly accurate MIC prediction models can be generated with fewer than 500 genomes. We also show that our approach for predicting MICs is stable over time despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predictingmore »
- Authors:
-
- Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
- Houston Medical Research Inst. and Houston Medical Hospital, Houston, TX (United States); Weill Cornell Medical College, New York, NY (United States)
- U.S. Food and Drug Administration, Laurel, MD (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, Chicago, IL (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID); USDOE
- OSTI Identifier:
- 1494667
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Clinical Microbiology
- Additional Journal Information:
- Journal Volume: 57; Journal Issue: 2; Journal ID: ISSN 0095-1137
- Publisher:
- American Society for Microbiology
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; antimicrobial susceptibility testing; deep learning; diagnostics; genome sequencing; machine learning
Citation Formats
Nguyen, Marcus, Long, S. Wesley, McDermott, Patrick F., Olsen, Randall J., Olson, Robert, Stevens, Rick L., Tyson, Gregory H., Zhao, Shaohua, and Davis, James J. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. United States: N. p., 2019.
Web. doi:10.1128/JCM.01260-18.
Nguyen, Marcus, Long, S. Wesley, McDermott, Patrick F., Olsen, Randall J., Olson, Robert, Stevens, Rick L., Tyson, Gregory H., Zhao, Shaohua, & Davis, James J. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. United States. https://doi.org/10.1128/JCM.01260-18
Nguyen, Marcus, Long, S. Wesley, McDermott, Patrick F., Olsen, Randall J., Olson, Robert, Stevens, Rick L., Tyson, Gregory H., Zhao, Shaohua, and Davis, James J. Wed .
"Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella". United States. https://doi.org/10.1128/JCM.01260-18. https://www.osti.gov/servlets/purl/1494667.
@article{osti_1494667,
title = {Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella},
author = {Nguyen, Marcus and Long, S. Wesley and McDermott, Patrick F. and Olsen, Randall J. and Olson, Robert and Stevens, Rick L. and Tyson, Gregory H. and Zhao, Shaohua and Davis, James J.},
abstractNote = {Nontyphoidal Salmonella species are the leading bacterial cause of food-borne 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. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, were used to generate XGBoost-based machine learning models for predicting minimum inhibitory concentrations (MICs) for 15 antibiotics. The MIC prediction models have an overall average accuracy of 95% within ± 1 two-fold dilution step (confidence interval of 95-95%), an average very major error rate of 2.7% (confidence interval of 2.4-3.0%) and an average major error rate of 0.1% (confidence interval of 0.1-0.2%). The model predicts MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for training sets, we show that highly accurate MIC prediction models can be generated with fewer than 500 genomes. We also show that our approach for predicting MICs is stable over time despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Furthermore, our strategy for developing whole genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.},
doi = {10.1128/JCM.01260-18},
journal = {Journal of Clinical Microbiology},
number = 2,
volume = 57,
place = {United States},
year = {2019},
month = {1}
}
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