Predicting rifampicin resistance in <i>Mycobacterium tuberculosis</i> using machine learning informed by protein structural and chemical features
It is possible to predict whether a mutation in <i>rpoB</i> confers resistance to rifampicin using a machine learning model trained on a combination of structural, chemical and evolutionary features; however, performance is moderate and training is complicated by the lack of data.
Authors
- Philip W. Fowler (ORCID: https://orcid.org/0000-0003-0912-4483)
- Charlotte I. Lynch (ORCID: https://orcid.org/0000-0001-6619-6331)
- Dylan Adlard (ORCID: https://orcid.org/0009-0004-1595-2095)
Institutions
- John Radcliffe Hospital (GB)
- University of Oxford (GB)
Publication Details
- Journal
- ERJ Open Research
- Published
- 2025-01-16
- DOI
- https://doi.org/10.1183/23120541.00952-2024
- Citations
- 5
- Primary Topic
- Tuberculosis Research and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 6.98
Funders
- National Institute for Health Research Health Protection Research Unit
- Horizon 2020 Framework Programme
- NIHR Oxford Biomedical Research Centre