Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

Journal: BMC infectious diseases
PMID: 39639222

Abstract

BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predicting drug resistance of four anti-TB drugs (rifampicin, isoniazid, streptomycin, and ethambutol) in MTB using whole-genome sequence and clinical data from Uganda. We also assessed the model's generalizability on another dataset from South Africa.

Authors

  • Sandra Ruth Babirye
    Department of Immunology and Molecular Biology, School of Biomedical Sciences, College of Health Sciences, Makerere University, P.O. Box 7072, Kampala, Uganda.
  • Mike Nsubuga
    The African Center of Excellence in Bioinformatics and Data-Intensive Science (ACE), Kampala, Uganda.
  • Gerald Mboowa
    Department of Immunology and Molecular Biology, School of Biomedical Sciences, College of Health Sciences, Makerere University, P.O. Box 7072, Kampala, Uganda.
  • Charles Batte
    Lung Institute, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.
  • Ronald Galiwango
    Centre for Computational Biology, Uganda Christian University, Mukono, Uganda.
  • David Patrick Kateete
    Department of Molecular Biology, College of Health Sciences, Makerere University, Kampala, Uganda.

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