ArticleEMBO molecular medicine2020
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.
Article in EMBO molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 5 of them syntheses that pooled it.
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Who cites it
114 citing papers in PubMed, 5 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
- Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis.Frontiers in medicine · 2025Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Pooled it
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Response to comment on "Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper".PLoS computational biology · 2026Article
- ALPAR: automated learning pipeline for antimicrobial resistance.Bioinformatics (Oxford, England) · 2026Article
- Transcriptome-Integrated Metabolic Modeling Identifies Candidate Metabolic Adjuvants in Antibiotic-ResistantAntibiotics (Basel, Switzerland) · 2026Article
- MiaA-mediated tRNA modifications couple tryptophan attenuation and changes in tRNA abundance to complex phenotypes in Pseudomonas aeruginosa.Nucleic acids research · 2026Article
- Antimicrobial Resistance in Veterinary Bacterial Pathogens: Resistance Patterns, Zoonotic Risks and One Health Implications.Pathogens (Basel, Switzerland) · 2026Review
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- A Tumor-Agnostic, Topology-Informed Scoring Framework for Drug Repurposing: Application to CDK4/6 Inhibitor Resistance in HRBiomedicines · 2026Article
- Evaluation of a machine learning system for genomic antimicrobial susceptibility determination on a clinically representative test set.Microbiology spectrum · 2026Article
- Article
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- RNA modifications shape innate immunity and cellular adaptation during bacterial respiratory infection.Frontiers in immunology · 2026Review
- Artificial intelligence in optimizing antimicrobial therapy for gastro-renal disorders.Frontiers in cellular and infection microbiology · 2026Review
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Defence systems drive accessory genome interactions inISME communications · 2026Article
- Assessment for antibiotic resistance inVirulence · 2025Article
54 more citing papers are in PubMed but not listed here.
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15 authors.
Funding
Abstract
Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.
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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.