ArticlePLoS computational biology2020
Predicting antimicrobial resistance using conserved genes.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Aminoglycosides enhance meropenem/vaborbactam activity against KPC-producingAntimicrobial agents and chemotherapy · 2026Article
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents.Bioinformatics advances · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Gene copy-number features generalize better than SNPs for antimicrobial resistance prediction in Staphylococcus aureus.npj antimicrobials and resistance · 2025Article
- Pangenomic Characterization ofPathogens (Basel, Switzerland) · 2025Article
- Carbapenem Resistance inMicroorganisms · 2025Review
- Optimizing sample preparation for culture-free nanopore sequencing to enable rapid pathogen and antimicrobial resistance profiling in bovine mastitis.Frontiers in microbiology · 2025Article
- Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.PLoS computational biology · 2024Article
- Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Accessory genes define species-specific routes to antibiotic resistance.Life science alliance · 2024Article
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species.Nature communications · 2023Article
- Predicting variable gene content inmSystems · 2023Article
- Machine Learning of the Whole Genome Sequence ofMicroorganisms · 2023Review
- Feature weighted models to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences.Bioinformatics (Oxford, England) · 2023Article
- Machine learning and feature extraction for rapid antimicrobial resistance prediction ofFrontiers in microbiology · 2023Article
- Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective.Clinical microbiology reviews · 2022Review
- Presence and antimicrobial resistance profiles of Escherichia coli, Enterococcusspp. and Salmonellasp. in 12 species of Australian shorebirds and terns.Zoonoses and public health · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
A growing number of studies are using machine learning models to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. Although these studies are showing promise, the models are typically trained using features derived from comprehensive sets of AMR genes or whole genome sequences and may not be suitable for use when genomes are incomplete. In this study, we explore the possibility of predicting AMR phenotypes using incomplete genome sequence data. Models were built from small sets of randomly-selected core genes after removing the AMR genes. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in cases where the primary AMR mechanisms result from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes, we show that F1 scores and error rates are stable and have little variance between replicates. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes.
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Registered trials
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