ArticleNature communications2018
Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 2 of them syntheses that pooled it.
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Who cites it
100 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Pooled it
- Integrated multi-omics analysis of fluoroquinolone tolerance mechanisms induced by enrofloxacin in Pasteurella multocida.BMC microbiology · 2026Article
- Genome-based source attribution using a One HealthMicrobial genomics · 2026Article
- Article
- A leakage-aware genomic prediction pipeline for meropenem resistance inBioinformatics advances · 2026Article
- The pangenome: a statistical model, not a fixed biological property.Bioinformatics advances · 2026Review
- Machine learning-based lineage prediction from antimicrobial susceptibility testing phenotypes forMicrobial genomics · 2026Article
- Genomic Surveillance of 3R Genes Associated with Antibiotic Resistance inAntibiotics (Basel, Switzerland) · 2025Article
- Assessment for antibiotic resistance inVirulence · 2025Article
- Exploiting Salmonella typhimurium as a novel cancer therapeutic agent: from tumor colonization to metabolic engineering.World journal of microbiology & biotechnology · 2025Review
- Phosphoglucomutase A-mediated metabolic adaptation is essential for antibiotic and disease persistence inmSystems · 2025Article
- Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.BMC genomic data · 2025Article
- Pitfalls of bacterial pan-genome analysis approaches: a case study of Mycobacterium tuberculosis and two less clonal bacterial species.Bioinformatics (Oxford, England) · 2025Article
- Interred mechanisms of resistance and host immune evasion revealed through network-connectivity analysis ofmSystems · 2025Article
- How AI can help us beat AMR.npj antimicrobials and resistance · 2025Review
- Article
- aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction inFrontiers in genetics · 2025Article
- Beyond H37Rv:Frontiers in microbiology · 2025Review
- Feature selection and aggregation for antibiotic resistance GWAS inFrontiers in microbiology · 2025Article
40 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens.
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