Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis
This study integrates ML methods into antimicrobial resistance research, develops a framework for predicting resistance phenotypes, and explores AMR-related SNPs in MTB. Quantifying the important SNPs' contribution to model decisions makes the ML algorithmic process more transparent, interpretable enabling and enables clinical practice.
Authors
- Zubi Liu
- Yunsong Yu (ORCID: https://orcid.org/0000-0003-2903-918X)
- Xiaoting Hua (ORCID: https://orcid.org/0000-0001-8215-916X)
- Hongrui Zhang (ORCID: https://orcid.org/0000-0003-3215-711X)
- Zhichao Wang (ORCID: https://orcid.org/0000-0002-4237-5084)
- Yan Jiang (ORCID: https://orcid.org/0000-0002-5877-9286)
- Lingling Lu (ORCID: https://orcid.org/0000-0001-6615-5409)
- Yi Zou (ORCID: https://orcid.org/0000-0001-8722-8144)
- Ying Mao (ORCID: https://orcid.org/0000-0001-8055-115X)
- Xu Yi
Institutions
- Sir Run Run Shaw Hospital (CN)
- Zhejiang Hospital (CN)
- Zhejiang Institute of Mechanical and Electrical Engineering (CN)
- Dian Diagnostics (China) (CN)
- Zhejiang University of Technology (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- BMC Genomic Data
- Published
- 2025-07-12
- DOI
- https://doi.org/10.1186/s12863-025-01338-x
- Citations
- 12
- Primary Topic
- Tuberculosis Research and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 16.75