Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal Salmonella enterica Serovars by Using Machine Learning
Identifieur interne : 000535 ( Main/Exploration ); précédent : 000534; suivant : 000536Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal Salmonella enterica Serovars by Using Machine Learning
Auteurs : Finlay Maguire [Canada] ; Muhammad Attiq Rehman [Canada] ; Catherine Carrillo [Canada] ; Moussa S. Diarra [Canada] ; Robert G. Beiko [Canada]Source :
- mSystems [ 2379-5077 ] ; 2019.
Abstract
Antimicrobial resistance (AMR) represents an existential threat to the function of modern medicine. Genomics and machine learning methods are being increasingly used to analyze and predict AMR. This type of surveillance is very important to try to reduce the impact of AMR. Machine learning models are typically trained using genomic data, but the aspects of the genomes that they use to make predictions are rarely analyzed. In this work, we showed how, by using different types of machine learning models and performing this analysis, it is possible to identify the key genes underlying AMR in nontyphoidal
Url:
DOI: 10.1128/mSystems.00211-19
PubMed: 31387929
PubMed Central: 6687941
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Antimicrobial resistance (AMR) represents an existential threat to the function of modern medicine. Genomics and machine learning methods are being increasingly used to analyze and predict AMR. This type of surveillance is very important to try to reduce the impact of AMR. Machine learning models are typically trained using genomic data, but the aspects of the genomes that they use to make predictions are rarely analyzed. In this work, we showed how, by using different types of machine learning models and performing this analysis, it is possible to identify the key genes underlying AMR in nontyphoidal <italic>Salmonella</italic>
(NTS). NTS is among the leading cause of foodborne illness globally; however, AMR in NTS has not been heavily studied within the food chain itself. Therefore, in this work we performed a broad-scale analysis of the AMR in NTS isolates from commercial chicken farms and identified some priority AMR genes for surveillance.</p>
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