ReviewFrontiers in microbiology2024
Artificial intelligence tools for the identification of antibiotic resistance genes.
Review in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Profiling the gut resistome to unlock antimicrobial resistance biology and inform clinical risk.Nature communications · 2026Review
- Future-proofing tuberculosis therapy: framework for concurrent drug and resistance testing development.The Lancet. Infectious diseases · 2026Review
- Metabolism-Based Biomarkers for Rapid Phenotypic Antibiotic Susceptibility Testing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Investigating the distribution of antibiotic resistance genes in relation to bacterial, fungal, and functional diversity in a hay field.Microbiology spectrum · 2026Article
- Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems.npj antimicrobials and resistance · 2026Review
- Genome-wide screening of antibiotic survival genes in Escherichia coli MG1655.BMC microbiology · 2026Article
- Effect of probiotics and synbiotics on antimicrobial resistance in frequent infections: a systematic review of clinical trials.Annals of medicine and surgery (2012) · 2026Article
- Carbapenem-resistant Gram-negative pathogens: molecular epidemiology, diagnostic advances, and emerging therapeutic strategies.Frontiers in microbiology · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.One health outlook · 2025Article
- Metagenomic Next-Generation Sequencing in Infectious Diseases: Clinical Applications, Translational Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Carbapenem-ResistantAntibiotics (Basel, Switzerland) · 2025Review
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
- Role of artificial intelligence in bacterial diagnostics and surveillance of anti-microbial resistance.Digital healthReview
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The fight against bacterial antibiotic resistance must be given critical attention to avert the current and emerging crisis of treating bacterial infections due to the inefficacy of clinically relevant antibiotics. Intrinsic genetic mutations and transferrable antibiotic resistance genes (ARGs) are at the core of the development of antibiotic resistance. However, traditional alignment methods for detecting ARGs have limitations. Artificial intelligence (AI) methods and approaches can potentially augment the detection of ARGs and identify antibiotic targets and antagonistic bactericidal and bacteriostatic molecules that are or can be developed as antibiotics. This review delves into the literature regarding the various AI methods and approaches for identifying and annotating ARGs, highlighting their potential and limitations. Specifically, we discuss methods for (1) direct identification and classification of ARGs from genome DNA sequences, (2) direct identification and classification from plasmid sequences, and (3) identification of putative ARGs from feature selection.
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What OpenQuestion holds
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.