Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens

被引:0
|
作者
Yun, Bona [1 ,2 ]
Liao, Xinyu [1 ,2 ]
Feng, Jinsong [1 ]
Ding, Tian [1 ,2 ,3 ]
机构
[1] Zhejiang Univ, Dept Food Sci & Nutr, Hangzhou, Peoples R China
[2] Zhejiang Univ, Innovat Ctr Yangtze River Delta, Future Food Lab, Jiaxing, Peoples R China
[3] Zhejiang Univ, Dept Food Sci & Nutr, Hangzhou 310058, Peoples R China
基金
中国国家自然科学基金;
关键词
Foodborne pathogens; food safety; antimicrobial resistance; machine learning; ANTIBIOTIC-RESISTANCE; STAPHYLOCOCCUS-AUREUS; FOOD-ANIMALS; SEQUENCE; SALMONELLA; MECHANISMS; DISCOVER; POULTRY; TOOL;
D O I
10.1080/19476337.2024.2324024
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
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.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics
    Khaledi, Ariane
    Weimann, Aaron
    Schniederjans, Monika
    Asgari, Ehsaneddin
    Kuo, Tzu-Hao
    Oliver, Antonio
    Cabot, Gabriel
    Kola, Axel
    Gastmeier, Petra
    Hogardt, Michael
    Jonas, Daniel
    Mofrad, Mohammad R. K.
    Bremges, Andreas
    McHardy, Alice C.
    Haeussler, Susanne
    [J]. EMBO MOLECULAR MEDICINE, 2020, 12 (03)
  • [2] Machine Learning-Enabled Noncontact Sleep Structure Prediction
    Zhai, Qian
    Tang, Tingyu
    Lu, Xiaoling
    Zhou, Xiaoxi
    Li, Chunguang
    Yi, Jingang
    Liu, Tao
    [J]. ADVANCED INTELLIGENT SYSTEMS, 2022, 4 (05)
  • [3] Antimicrobial resistance of foodborne pathogens
    White, DG
    Zhao, SH
    Simjee, S
    Wagner, DD
    McDermott, PF
    [J]. MICROBES AND INFECTION, 2002, 4 (04) : 405 - 412
  • [4] Machine Learning-enabled Scalable Performance Prediction of Scientific Codes
    Chennupati, Gopinath
    Santhi, Nandakishore
    Romero, Phill
    Eidenbenz, Stephan
    [J]. ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION, 2021, 31 (02):
  • [5] Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides
    Mushtaq Ahmad Wani
    Prabha Garg
    Kuldeep K. Roy
    [J]. Medical & Biological Engineering & Computing, 2021, 59 : 2397 - 2408
  • [6] Machine Learning-Enabled Design and Prediction of Protein Resistance on Self-Assembled Monolayers and Beyond
    Liu, Yonglan
    Zhang, Dong
    Tang, Yijing
    Zhang, Yanxian
    Chang, Yung
    Zheng, Jie
    [J]. ACS APPLIED MATERIALS & INTERFACES, 2021, 13 (09) : 11306 - 11319
  • [7] Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides
    Wani, Mushtaq Ahmad
    Garg, Prabha
    Roy, Kuldeep K.
    [J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2021, 59 (11-12) : 2397 - 2408
  • [8] Machine learning-enabled retrobiosynthesis of molecules
    Yu, Tianhao
    Boob, Aashutosh Girish
    Volk, Michael J.
    Liu, Xuan
    Cui, Haiyang
    Zhao, Huimin
    [J]. NATURE CATALYSIS, 2023, 6 (2) : 137 - 151
  • [9] Machine learning-enabled prediction of high-temperature oxidation resistance for Ni-based alloys
    Li, Changheng
    Xu, Kai
    Lou, Ming
    Wang, Linjing
    Chang, Keke
    [J]. CORROSION SCIENCE, 2024, 234
  • [10] Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products
    Yuan, Yujie
    Shi, Chengyou
    Zhao, Huimin
    [J]. ACS SYNTHETIC BIOLOGY, 2023, 12 (09): : 2650 - 2662