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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\n            <jats:italic>Salmonella<\/jats:italic>\n            (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.\n          <\/jats:p>","DOI":"10.1128\/msystems.00211-19","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T12:29:30Z","timestamp":1565008170000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":38,"title":["Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal Salmonella enterica Serovars by Using Machine Learning"],"prefix":"10.1128","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1203-9514","authenticated-orcid":false,"given":"Finlay","family":"Maguire","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-1969","authenticated-orcid":false,"given":"Muhammad Attiq","family":"Rehman","sequence":"additional","affiliation":[{"name":"Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Carrillo","sequence":"additional","affiliation":[{"name":"Canadian Food Inspection Agency (CFIA), Ottawa, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2966-345X","authenticated-orcid":false,"given":"Moussa S.","family":"Diarra","sequence":"additional","affiliation":[{"name":"Guelph Research and Development Center, Agriculture and Agri-Food Canada (AAFC), Guelph, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5065-4980","authenticated-orcid":false,"given":"Robert G.","family":"Beiko","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrg.2017.88"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/trstmh\/trw048"},{"key":"e_1_3_2_4_2","volume-title":"Global action plan on antimicrobial resistance.","author":"World Health Organization","year":"2015","unstructured":"World Health Organization. 2015. 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