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

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.

Summary

Given the availability of DNA sequencing data from MTB, we developed machine learning models for a cohort of 1839 UK bacterial isolates to classify MTB resistance against eight anti-TB drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, ciprofloxacin, moxifloxacin, ofloxacin, streptomycin) and to classify multi-drug resistance.

Results

Compared to previous rules-based

SUBMITTER: Yang Y 

PROVIDER: S-EPMC5946815 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature