ArticleScientific reports2020
Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- On the usage of artificial intelligence in leprosy care: A systematic literature review.PLoS computational biology · 2025Pooled it
- The structural context of mutations in proteins predicts their effect on antibiotic resistance.eLife · 2026Article
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Machine Learning Prediction of Multidrug Resistance in Swine-DerivedVeterinary sciences · 2025Article
- RpoB mutation patterns in Rifampicin-resistant tuberculosis: a Jiangxi Province study, 2021-2023.Scientific reports · 2025Article
- Artificial intelligence for tuberculosis control: a scoping review of applications in public health.Journal of global health · 2025Article
- Predicting rifampicin resistance inERJ open research · 2025Article
- Comprehensive genome analysis ofVeterinary world · 2025Article
- Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges.Human genetics · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Engineering G protein-coupled receptors for stabilization.Protein science : a publication of the Protein Society · 2024Article
- Phylogenetic Analysis and Comparative Genomics of Brucella abortus and Brucella melitensis Strains in Egypt.Journal of molecular evolution · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Prediction of pyrazinamide resistance inJAC-antimicrobial resistance · 2024Article
- Environmental and genetic influence on the rate and spectrum of spontaneous mutations inMicrobiology (Reading, England) · 2024Article
- Overcoming antibiotic resistance: non-thermal plasma and antibiotics combination inhibits important pathogens.Pathogens and disease · 2024Review
- Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease.Human molecular genetics · 2024Article
- Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach.Nature communications · 2024Article
- Evaluation of mycobacteria infection prevalence and optimization of the identification process in North Sardinia, Italy.Microbiology spectrum · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.