{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T12:03:50Z","timestamp":1790597030208,"version":"4.1.0"},"reference-count":41,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Foodborne Pathogens and Disease"],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:p>\n                    Traditional experimental methods for determining antibiotic resistance phenotypes (ARPs) and minimum inhibitory concentrations (MICs) in bacteria are laborious and time consuming. This study aims to explore the potential of whole-genome sequencing data combined with machine learning models for robustly predicting ARPs and MICs in\n                    <jats:italic toggle=\"yes\">Salmonella<\/jats:italic>\n                    . Using a training set of 6394\n                    <jats:italic toggle=\"yes\">Salmonella<\/jats:italic>\n                    genomes alongside antimicrobial susceptibility testing results, we built two machine learning (ML) predictive models based on the pan-genome and pan-resistome. Each model was implemented using three algorithms: random forest, extreme gradient boosting (XGB), and convolutional neural network. Among them, XGB achieved the highest overall accuracy, with the pan-genome and pan-resistome models accurately predicting ARPs (98.51% and 97.77%) and MICs (81.42% and 78.99%) for 15 commonly used antibiotics. Feature extraction from pan-genome and pan-resistome data effectively reduced computational complexity and significantly decreased computation time. Notably, fewer than 10 key genomic features, often linked to known resistance or mobile genes, were sufficient for robust predictions for each antibiotic. This study also identified challenges, including imbalanced resistance classes and imprecise MIC measurements, which impacted prediction accuracy. These findings highlight the importance of using multiple evaluation metrics to assess model performance comprehensively. Overall, our findings demonstrated that ML, utilizing pan-genome or pan-resistome features, was highly effective in predicting antibiotic resistance and identifying correlated genetic features in\n                    <jats:italic toggle=\"yes\">Salmonella<\/jats:italic>\n                    . This approach holds great potential to supplement conventional culture-based methods for routine surveillance of antibiotic-resistant bacteria.\n                  <\/jats:p>","DOI":"10.1089\/fpd.2024.0170","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T12:08:44Z","timestamp":1749816524000},"page":"648-656","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Prediction of Antibiotic Resistance Phenotypes and Minimum Inhibitory Concentrations in\n                    <i>Salmonella<\/i>\n                    Using Machine Learning Analysis of Its Pan-Genome and Pan-Resistome Features"],"prefix":"10.1177","volume":"23","author":[{"given":"Yichen","family":"He","sequence":"first","affiliation":[{"name":"Department of Food Science and Technology, MOST-USDA Joint Research Center for Food Safety and NMPA Key Laboratory for Testing Technology of Pharmaceutical Microbiology, School of Agriculture &amp; Biology, Shanghai Jiao Tong University, Shanghai, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiujuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Food Science and Technology, MOST-USDA Joint Research Center for Food Safety and NMPA Key Laboratory for Testing Technology of Pharmaceutical Microbiology, School of Agriculture &amp; Biology, Shanghai Jiao Tong University, Shanghai, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lida","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Food Science and Technology, MOST-USDA Joint Research Center for Food Safety and NMPA Key Laboratory for Testing Technology of Pharmaceutical Microbiology, School of Agriculture &amp; Biology, Shanghai Jiao Tong University, Shanghai, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Food Science and Technology, MOST-USDA Joint Research Center for Food Safety and NMPA Key Laboratory for Testing Technology of Pharmaceutical Microbiology, School of Agriculture &amp; Biology, Shanghai Jiao Tong University, Shanghai, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiping","family":"He","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service, USDA-MOST Joint Research Center for Food Safety and Eastern Regional Research Center, Wyndmoor, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Gehring","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service, USDA-MOST Joint Research Center for Food Safety and Eastern Regional Research Center, Wyndmoor, Pennsylvania, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Deng","sequence":"additional","affiliation":[{"name":"Center for Food Safety, Department of Food Science and Technology, University of Georgia, Griffin, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2883-0386","authenticated-orcid":false,"given":"Xianming","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Food Science and Technology, MOST-USDA Joint Research Center for Food Safety and NMPA Key Laboratory for Testing Technology of Pharmaceutical Microbiology, School of Agriculture &amp; Biology, Shanghai Jiao Tong University, Shanghai, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/jac\/dkh221"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1099\/mic.0.000940"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1128\/AAC.49.2.836-839.2005"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/2046-0481-66-23"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms10063"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-421"},{"key":"e_1_3_3_8_1","first-page":"785","article-title":"XGBoost: A scalable tree boosting system","author":"Chen T","year":"2016","unstructured":"Chen T, , Guestrin C. 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