ArticleERJ open research2025
Predicting rifampicin resistance in
Article in ERJ open research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- The structural context of mutations in proteins predicts their effect on antibiotic resistance.eLife · 2026Article
- Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.BMC microbiology · 2026Article
- Subpopulations in clinical samples ofJAC-antimicrobial resistance · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rifampicin remains a key antibiotic in the treatment of tuberculosis. Despite advances in cataloguing resistance-associated variants (RAVs), novel and rare mutations in the relevant gene, Methods: We built a Test+Train dataset comprising 219 susceptible mutations and 46 RAVs. Features derived from the structure of the RNA polymerase or the change in chemistry introduced by the mutation were considered; however, only a few, notably the distance from the rifampicin binding site, were found to be predictive on their own. Owing to the paucity of RAVs we used Monte Carlo cross-validation with 50 repeats to train four different machine learning models. Results: All four models behaved similarly with sensitivities and specificities in the range 0.84-0.88 and 0.94-0.97, although we preferred the ensemble of decision tree models as they are easy to inspect and understand. We showed that measuring distances from molecular dynamics simulations did not improve performance. Conclusions: It is possible to predict whether a mutation in
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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.