Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella

2018/10/12 by Marcus Nguyen, S. Wesley Long, Patrick F. McDermott +7 · 2 citations
Agricultural and Biological Sciences · Medicine · #Salmonella and Campylobacter epidemiology #Probiotics and Fermented Foods #Viral gastroenteritis research and epidemiology

paper · pdf · doi:10.1128/jcm.01260-18

Abstract

information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for the training sets, we show that highly accurate MIC prediction models can be generated with less 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. Our strategy for developing whole-genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

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