Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data.

Journal: Bioinformatics (Oxford, England)
PMID: 29240876

Abstract

MOTIVATION: Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification.

Authors

  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Katherine E Niehaus
    Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.
  • Timothy M Walker
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
  • Zamin Iqbal
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
  • A Sarah Walker
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
  • Daniel J Wilson
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
  • Tim E A Peto
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
  • Derrick W Crook
    Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
  • E Grace Smith
    Public Health England, Colindale, London, UK.
  • Tingting Zhu
    Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.
  • David A Clifton

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