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. 2019 Jul 1;35(13):2276-2282.
doi: 10.1093/bioinformatics/bty949.

Application of machine learning techniques to tuberculosis drug resistance analysis

Samaneh Kouchaki  1 , Yang Yang  1 , Timothy M Walker  2   3 , A Sarah Walker  2   3   4 , Daniel J Wilson  5 , Timothy E A Peto  2   3 , Derrick W Crook  2   3   6 ; CRyPTIC Consortium; David A Clifton  1
Affiliations

Application of machine learning techniques to tuberculosis drug resistance analysis

Samaneh Kouchaki et al. Bioinformatics. .

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.

Availability and implementation: The source code can be found at http://www.robots.ox.ac.uk/ davidc/code.php.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Classification performance (AUC%) considering six machine learning classifiers (LR with L1 and L2 regularization terms, SVM with Linear and RBF kernels, RF, Adaboost, PM and GBT) across 11 anti-TB drugs and F1 feature space
Fig. 2.
Fig. 2.
Classification performance (AUC%) considering six machine learning classifiers across 11 anti-TB drugs with (a) SNMF-F1 and (b) SPCA-F1
See this image and copyright information in PMC

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