ArticlePLoS computational biology2020
Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens.
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 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.
- Pooled it
- Next-Generation Target Discovery in ESKAPE Pathogens: An AI-Driven Framework from Omics-Based to Systems-Level Modeling and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- Evaluation of a machine learning system for genomic antimicrobial susceptibility determination on a clinically representative test set.Microbiology spectrum · 2026Article
- Article
- Integrating artificial intelligence with genome sequencing against antimicrobial resistance: a narrative review.Frontiers in public health · 2026Review
- StaLAENet: A stacked LSTM-nested deep-autoencoder network for identification of antimicrobial resistance of nosocomial pathogens.Journal of biosciences · 2026Article
- Unveiling meropenem resistance and co-resistance patterns in Klebsiella pneumoniae and Acinetobacter baumannii: a global genome analysis using ML/DL and association mining.BMC microbiology · 2025Article
- Machine Learning Prediction of Multidrug Resistance in Swine-DerivedVeterinary sciences · 2025Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- Review
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- PredictingmSystems · 2024Article
- Review
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Article
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Artificial intelligence tools for the identification of antibiotic resistance genes.Frontiers in microbiology · 2024Review
- A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria.Frontiers in antibiotics · 2024Article
- Deciphering the genomic character of the multidrug-resistantAIMS microbiology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The evolution of antimicrobial resistance (AMR) poses a persistent threat to global public health. Sequencing efforts have already yielded genome sequences for thousands of resistant microbial isolates and require robust computational tools to systematically elucidate the genetic basis for AMR. Here, we present a generalizable machine learning workflow for identifying genetic features driving AMR based on constructing reference strain-agnostic pan-genomes and training random subspace ensembles (RSEs). This workflow was applied to the resistance profiles of 14 antimicrobials across three urgent threat pathogens encompassing 288 Staphylococcus aureus, 456 Pseudomonas aeruginosa, and 1588 Escherichia coli genomes. We find that feature selection by RSE detects known AMR associations more reliably than common statistical tests and previous ensemble approaches, identifying a total of 45 known AMR-conferring genes and alleles across the three organisms, as well as 25 candidate associations backed by domain-level annotations. Furthermore, we find that results from the RSE approach are consistent with existing understanding of fluoroquinolone (FQ) resistance due to mutations in the main drug targets, gyrA and parC, in all three organisms, and suggest the mutational landscape of those genes with respect to FQ resistance is simple. As larger datasets become available, we expect this approach to more reliably predict AMR determinants for a wider range of microbial pathogens.
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Registered trials
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