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

Yang Yang1, Katherine E Niehaus1, Timothy M Walker2

  • 1Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.

Bioinformatics (Oxford, England)
|December 15, 2017
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict Mycobacterium tuberculosis resistance to eight drugs, improving upon conventional methods for tuberculosis control. These models enhance sensitivity for classifying drug resistance, aiding in effective treatment strategies.

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Area of Science:

  • Microbiology and Infectious Diseases
  • Bioinformatics and Computational Biology
  • Genetics and Genomics

Background:

  • Accurate determination of Mycobacterium tuberculosis (MTB) drug resistance is crucial for tuberculosis (TB) management.
  • Conventional molecular diagnostics have limitations in sensitivity for classifying MTB resistance.
  • Existing methods often rely on identifying single nucleotide polymorphisms, which can be insufficient.

Purpose of the Study:

  • To develop and evaluate machine learning models for classifying MTB resistance to eight key anti-TB drugs.
  • To improve the sensitivity of resistance classification compared to existing rule-based approaches.
  • To classify multi-drug resistance in MTB isolates.

Main Methods:

  • Utilized DNA sequencing data from 1839 UK bacterial isolates.
  • Developed and applied machine learning models to predict drug resistance.
  • Compared model performance against a conventional rules-based approach.

Main Results:

  • The best-performing machine learning models demonstrated significantly improved sensitivity for classifying resistance to isoniazid, rifampicin, ethambutol, ciprofloxacin, moxifloxacin, ofloxacin, pyrazinamide, and streptomycin.
  • Sensitivity increases ranged from 2-4% for some drugs to as high as 15-24% for pyrazinamide and streptomycin.
  • Area-under-the-ROC curve improvements of 4-10% were observed, indicating enhanced classification accuracy.

Conclusions:

  • Machine learning models offer a more sensitive and accurate approach to classifying MTB drug resistance compared to traditional methods.
  • These models have the potential to enhance the management and control of tuberculosis by enabling faster and more precise treatment decisions.
  • The developed models and source code are available for further research and application.