Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.

Journal: EBioMedicine
Published Date: May 1, 2019

Abstract

BACKGROUND: The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data especially for certain drugs. Our primary aim was to perform an exploration of statistical learning algorithms and genetic predictor sets using a rich dataset to build a high performing and fast predicting model to detect anti-tuberculosis drug resistance.

Authors

  • Michael L Chen
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America.
  • Akshith Doddi
    University of Virginia School of Medicine, Charlottesville, VA, United States of America.
  • Jimmy Royer
    Analysis Group Inc., United States of America.
  • Luca Freschi
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America.
  • Marco Schito
    Critical Path Institute, 1730 E River Rd., Tucson, AZ, United States of America.
  • Matthew Ezewudo
    Critical Path Institute, 1730 E River Rd., Tucson, AZ, United States of America.
  • Isaac S Kohane
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Isaac_Kohane@hms.harvard.edu.
  • Andrew Beam
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America.
  • Maha Farhat
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America; Division of Pulmonary & Critical Care, Massachusetts General Hospital, Boston, MA, United States of America. Electronic address: Maha_Farhat@hms.harvard.edu.

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