{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T18:15:33Z","timestamp":1783707333839,"version":"3.55.0"},"reference-count":83,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Tuberculosis is a deadly airborne disease caused by\n                    <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                    . Drug-resistant tuberculosis presents significant challenges for treatment and control of the disease. Resistant strains of\n                    <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                    arise from specific mutations in the bacterium. Identification and characterization of resistance-associated mutations are crucial for effective treatment strategies because the first- and second-line drugs for the disease target distinct genes in the bacterium and serve different purposes. Our study developed a machine learning prediction model to analyze mutations across multiple drug-resistance types. The proposed framework predicts drug-resistance mutations across four drug-resistance types, including Rifampicin Resistance, Isoniazid Resistance, Multidrug Resistance, and Pre-extensively Drug-Resistant tuberculosis. The NIAID-NIH TB portal is a publicly available dataset of tuberculosis patients, including drug-resistance information. Our study analyzed 3,065 cases of drug-resistant TB. Eight supervised ML algorithms were implemented for the study. A Random Forest classifier with 10-fold cross-validation shows higher predictive performance than the other seven algorithms considered for further analysis. Significant drug resistance mutations were identified using SHapley Additive exPlanations feature importance. The World Health Organisation mutation catalogues, considered the gold standard for drug-resistant mutations, were used to evaluate prediction results. Mutations not reported in the WHO catalogues were identified during the post-prediction comparative analysis stage, as they may represent potential resistance-conferring markers warranting further investigation, including structural and functional validation or experimental validation. The mutations are\n                    <jats:italic>rpoB-<\/jats:italic>\n                    I480T,\n                    <jats:italic>rpoC<\/jats:italic>\n                    -G332R, L527V,\n                    <jats:italic>gyrA<\/jats:italic>\n                    -D94V,\n                    <jats:italic>KatG<\/jats:italic>\n                    -G99E, A106V, W191R, W328C, T380I, and M420T. The study further checks the stability and pathogenicity of the mutations using computational tools, including I-Mutant 2.0 and PredictSNP. The findings added more clarity and further evidence for the significance of the mutation, based on its contribution to drug resistance.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0352863","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T17:48:08Z","timestamp":1783705688000},"page":"e0352863","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8406-3923","authenticated-orcid":true,"given":"Athira","family":"Thankamani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biji","family":"C L","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5971-8290","authenticated-orcid":true,"given":"George Priya","family":"Doss C","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"pone.0352863.ref001","unstructured":"Global Tuberculosis Report. 2024. https:\/\/www.who.int\/teams\/global-tuberculosis-programme\/tb-reports\/global-tuberculosisreport-2024"},{"key":"pone.0352863.ref002","doi-asserted-by":"crossref","first-page":"101574","DOI":"10.1016\/j.cimid.2020.101574","article-title":"Drug resistant Tuberculosis: A review","volume":"74","author":"JL Khawbung","year":"2021","journal-title":"Comp Immunol Microbiol Infect Dis"},{"key":"pone.0352863.ref003","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijid.2021.02.107","article-title":"Global Tuberculosis Report 2020 \u2013 Reflections on the Global TB burden, treatment and prevention efforts","volume":"113","author":"J Chakaya","year":"2021","journal-title":"Int J Infectious Diseases"},{"key":"pone.0352863.ref004","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1056\/NEJMsr1512438","article-title":"Twenty Years of Global Surveillance of Antituberculosis-Drug Resistance","volume":"375","author":"M Zignol","year":"2016","journal-title":"New England J Med"},{"key":"pone.0352863.ref005","unstructured":"Guidance for the Surveillance of Drug Resistance in Tuberculosis. 2021."},{"key":"pone.0352863.ref006","doi-asserted-by":"crossref","first-page":"870768","DOI":"10.3389\/fimmu.2022.870768","article-title":"Tuberculosis phenotypic and genotypic drug susceptibility testing and immunodiagnostics: A review","volume":"13","author":"KA Yusoof","year":"2022","journal-title":"Front Immunol"},{"issue":"2","key":"pone.0352863.ref007","doi-asserted-by":"crossref","DOI":"10.1093\/ofid\/ofab018","article-title":"Rifampicin Mono-Resistant Tuberculosis-A Review of an Uncommon But Growing Challenge for Global Tuberculosis Control","volume":"8","author":"JH Malenfant","year":"2021","journal-title":"Open Forum Infect Dis"},{"key":"pone.0352863.ref008","article-title":"Rifampin mono-resistant tuberculosis in New York City, 2010\u20132021: A retrospective case series","author":"JA Lindsey","year":"2023","journal-title":"Open Forum Infect Dis"},{"issue":"11","key":"pone.0352863.ref009","doi-asserted-by":"crossref","DOI":"10.1128\/AAC.00364-21","article-title":"Rifampicin-monoresistant tuberculosis is not the same as multidrug-resistant tuberculosis: a descriptive study from Khayelitsha, South Africa","volume":"65","author":"Z Salaam-Dreyer","year":"2021","journal-title":"Antimicrob Agents Chemother"},{"issue":"1","key":"pone.0352863.ref010","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1186\/s12866-025-03816-9","article-title":"Prevalence of katG and inhA mutations associated with isoniazid resistance in Mycobacterium tuberculosis clinical isolates in Cameroon","volume":"25","author":"VN Nono","year":"2025","journal-title":"BMC Microbiol"},{"key":"pone.0352863.ref011","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.jgar.2020.02.026","article-title":"Mutations in rpoB and katG genes and the inhA operon in multidrug-resistant Mycobacterium tuberculosis isolates from Zambia","volume":"22","author":"ES Solo","year":"2020","journal-title":"J Glob Antimicrob Resist"},{"key":"pone.0352863.ref012","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijid.2020.01.042","article-title":"MDR\/XDR-TB management of patients and contacts: Challenges facing the new decade. 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