Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.

Journal: BMC genomic data
Published Date: Jul 12, 2025

Abstract

BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine learning on whole-genome sequencing data (WGS) presents significant potential for uncovering the genomic mechanisms underlying drug resistance in MTB.

Authors

  • Yi Xu
    School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
  • Ying Mao
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Xiaoting Hua
    Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310016, China.
  • Yan Jiang
    Department of Nursing/Evidence-based Nursing Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Yi Zou
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Zhichao Wang
  • Zubi Liu
    Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310016, China.
  • Hongrui Zhang
    School of Future Technology, South China University of Technology, Guangzhou, China.
  • Lingling Lu
    Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Science, Beijing 100190, China.
  • Yunsong Yu
    Department of Infectious Diseases, College of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China.

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