Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens
The World Health Organization (WHO) has identified antimicrobial resistance (AMR) as one of the top three global dangers to public health. One of the most vital factors contributing to the high prevalence of AMR is the misuse/overuse of antibiotics for treatment and/or as a growth promoter in the fo...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | CyTA - Journal of Food |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19476337.2024.2324024 |
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| author | Bona Yun Xinyu Liao Jinsong Feng Tian Ding |
| author_facet | Bona Yun Xinyu Liao Jinsong Feng Tian Ding |
| author_sort | Bona Yun |
| collection | DOAJ |
| description | The World Health Organization (WHO) has identified antimicrobial resistance (AMR) as one of the top three global dangers to public health. One of the most vital factors contributing to the high prevalence of AMR is the misuse/overuse of antibiotics for treatment and/or as a growth promoter in the food industry. AMR can be transmitted to humans via food, the environment, or other channels through horizontal gene transfer. Therefore, efficient methods are urgently needed to determine whether bacteria are resistant to antibiotics. This work provides a review of the advances in machine learning (ML) techniques for predicting and identifying AMR in foodborne pathogens. We also emphasize the groundbreaking potential of whole genome sequencing (WGS) and spectroscopy technologies combined with ML in the context of AMR detection. These offer enormous potential because of their unique characteristics, which can overcome inherent limits in existing detection approaches. |
| format | Article |
| id | doaj-art-d100ddce893849a4a5eabce767bfe879 |
| institution | OA Journals |
| issn | 1947-6337 1947-6345 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | CyTA - Journal of Food |
| spelling | doaj-art-d100ddce893849a4a5eabce767bfe8792025-08-20T02:37:17ZengTaylor & Francis GroupCyTA - Journal of Food1947-63371947-63452024-12-0122110.1080/19476337.2024.2324024Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogensBona Yun0Xinyu Liao1Jinsong Feng2Tian Ding3Department of Food Science and Nutrition, Zhejiang University, Hangzhou, ChinaDepartment of Food Science and Nutrition, Zhejiang University, Hangzhou, ChinaDepartment of Food Science and Nutrition, Zhejiang University, Hangzhou, ChinaDepartment of Food Science and Nutrition, Zhejiang University, Hangzhou, ChinaThe World Health Organization (WHO) has identified antimicrobial resistance (AMR) as one of the top three global dangers to public health. One of the most vital factors contributing to the high prevalence of AMR is the misuse/overuse of antibiotics for treatment and/or as a growth promoter in the food industry. AMR can be transmitted to humans via food, the environment, or other channels through horizontal gene transfer. Therefore, efficient methods are urgently needed to determine whether bacteria are resistant to antibiotics. This work provides a review of the advances in machine learning (ML) techniques for predicting and identifying AMR in foodborne pathogens. We also emphasize the groundbreaking potential of whole genome sequencing (WGS) and spectroscopy technologies combined with ML in the context of AMR detection. These offer enormous potential because of their unique characteristics, which can overcome inherent limits in existing detection approaches.https://www.tandfonline.com/doi/10.1080/19476337.2024.2324024Foodborne pathogensfood safetyantimicrobial resistancemachine learning |
| spellingShingle | Bona Yun Xinyu Liao Jinsong Feng Tian Ding Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens CyTA - Journal of Food Foodborne pathogens food safety antimicrobial resistance machine learning |
| title | Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens |
| title_full | Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens |
| title_fullStr | Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens |
| title_full_unstemmed | Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens |
| title_short | Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens |
| title_sort | machine learning enabled prediction of antimicrobial resistance in foodborne pathogens |
| topic | Foodborne pathogens food safety antimicrobial resistance machine learning |
| url | https://www.tandfonline.com/doi/10.1080/19476337.2024.2324024 |
| work_keys_str_mv | AT bonayun machinelearningenabledpredictionofantimicrobialresistanceinfoodbornepathogens AT xinyuliao machinelearningenabledpredictionofantimicrobialresistanceinfoodbornepathogens AT jinsongfeng machinelearningenabledpredictionofantimicrobialresistanceinfoodbornepathogens AT tianding machinelearningenabledpredictionofantimicrobialresistanceinfoodbornepathogens |