ArticleGenome medicine2021
GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.
Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Open-access genomic drug resistance prediction tools forJournal of clinical microbiology · 2026Pooled it
- The structural context of mutations in proteins predicts their effect on antibiotic resistance.eLife · 2026Article
- Algorithmic Determinants of Performance Heterogeneity in Whole-Genome Sequencing-Based Prediction of Drug Resistance inMicroorganisms · 2026Review
- Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response.Nature communications · 2026Article
- An explainable artificial intelligence framework reveals mutations associated with drug resistance inBiosafety and health · 2025Article
- Accuracy of SAM-TB Platform as a Diagnostic Assay for Detection of Mycobacterium tuberculosis Drug Resistance Versus TB-Proflier, PhyResSE, and Mykrobe: A Head-to-Head Comparison.Current microbiology · 2025Article
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.BMC genomic data · 2025Article
- Advantages of updated WHO mutation catalog combined with existing whole-genome sequencing-based approaches for Mycobacterium tuberculosis resistance prediction.Genome medicine · 2025Article
- Drug Resistance and Epidemiological Success of Modern Mycobacterium tuberculosis Lineages in Western India.The Journal of infectious diseases · 2025Article
- Predicting Drug Resistance inDiagnostics (Basel, Switzerland) · 2025Article
- Molecular typing of Mycobacterium tuberculosis: a review of current methods, databases, softwares, and analytical tools.FEMS microbiology reviews · 2025Review
- Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences ofBioinformatics advances · 2025Article
- Perspectives in clinical microbiology for combating multi-drug resistant bacterial infections.Frontiers in cellular and infection microbiology · 2025Review
- Detection of multidrug-resistance in Mycobacterium tuberculosis by phenotype- and molecular-based assays.Annals of clinical microbiology and antimicrobials · 2024Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- Pan-lineage Mycobacterium tuberculosis reference genome for enhanced molecular diagnosis.DNA research : an international journal for rapid publication of reports on genes and genomes · 2024Article
- Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.Bioinformatics (Oxford, England) · 2024Article
- Combating antimicrobial resistance in malaria, HIV and tuberculosis.Nature reviews. Drug discovery · 2024Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
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9 authors.
Funding
Abstract
backgroundMultidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens.
resultsWe present Translational Genomics platform for Tuberculosis (GenTB), a free and open web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB's predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. All four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs were 77.6% (95% CI 76.6-78.5%) and 75.4% (95% CI 74.5-76.4%), respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4-75.3%) and Mykrobe at 71.9% (95% CI 70.9-72.9%). The higher sensitivities were at an expense of ≤ 1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5-97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Averaged across the four tools, genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (< 10× across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants
conclusionsGenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu , and the source code is available at https://github.com/farhat-lab/gentb-site .
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