Year: 2018
Type: article
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.
Source: Bioinformatics
Authors: Samaneh Kouchaki, Yang Yang, Timothy M Walker, A Sarah Walker, Daniel J Wilson, Timothy E A Peto, Derrick W Crook, David A Clifton
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,262
Awards: G1198837610, G1628705071, NIHR Senior Investigator Award, G2006052962, University of Oxford - to improve control of tuberculosis, and facilitate the WHO’s initiative to eliminate tuberculosis by 2050, by better, faster and more targeted trea..., Host directed therapies against tuberculosis., Machine Learning for Patient-Specific, Predictive Healthcare Technologies via Intelligent Electronic Health Records, NIHR Senior Investigator Award, G5610298320, G5738782026, Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC), G6199535770, Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC), G6868616705, G7196713885, Statistical Methods for Whole Genome Phenotype Mapping in Bacterial Populations., G9000226936
OpenAlex ID: W2901155787