GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.

Journal: Genome medicine
PMID: 34461978

Abstract

BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens.

Authors

  • Matthias I Gröschel
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Martin Owens
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Luca Freschi
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America.
  • Roger Vargas
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Maximilian G Marin
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Jody Phelan
    London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
  • Zamin Iqbal
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
  • Avika Dixit
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Maha R Farhat
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. maha_farhat@hms.harvard.edu.

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