Application of machine learning techniques to tuberculosis drug resistance analysis

CRyPTIC Consortium

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16 Citations (Scopus)

Abstract

Motivation: Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. Results: Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P < 0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers.

Original languageEnglish
Pages (from-to)2276-2282
Number of pages7
JournalBioinformatics
Volume35
Issue number13
DOIs
Publication statusPublished - 1 Jul 2019

Bibliographical note

Funding Information:
The work was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre; the CRyPTIC consortium which is funded by a Wellcome Trust/Newton Fund-MRC Collaborative Award [200205/Z/15/Z] and the Bill and Melinda Gates Foundation Trust [OPP1133541]. D.J.W. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society [101237/Z/13/Z]. SK was also supported by EPSRC (Grant EP/N020774/1).

Publisher Copyright:
© The Author(s) 2018. Published by Oxford University Press.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

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