ArticleScientific reports2016
Antimicrobial Resistance Prediction in PATRIC and RAST.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 4 of them syntheses that pooled it.
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
127 citing papers in PubMed, 4 syntheses or guidelines pooled it, 243 citations in OpenAlex.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Pooled it
- Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.PLoS computational biology · 2019Pooled it
- Artificial Intelligence in Rapid Diagnostics for Antimicrobial Resistance: From Bench to Bedside.Health science reports · 2026Article
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus.Interdisciplinary sciences, computational life sciences · 2026Article
- Isolation and characterization of probiotic candidates with antimicrobial and barrier-protective properties.Scientific reports · 2026Article
- resLens: genomic language models to enhance antibiotic resistance gene detection.npj antimicrobials and resistance · 2026Article
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Genomic Characterization of Multidrug-Resistant Myroides Odoratimimus from Pneumonic Piglets: A One Health Threat.Current microbiology · 2026Article
- BV-BRC: a unified bacterial and viral bioinformatics resource with expanded functionality and AI integration.Nucleic acids research · 2026Article
- A leakage-aware genomic prediction pipeline for meropenem resistance inBioinformatics advances · 2026Article
- Gene copy-number features generalize better than SNPs for antimicrobial resistance prediction in Staphylococcus aureus.npj antimicrobials and resistance · 2025Article
- Artificial Intelligence in Infectious Disease Diagnostic Technologies.Diagnostics (Basel, Switzerland) · 2025Review
- HKDE-LACM: a hybrid model for lactic acid bacteria classification via k-mer and DNABERT-2 embedding fusion with cyclic DE-BO optimization.BMC genomics · 2025Article
- Comparison of virulence and resistance genes inMicrobiology spectrum · 2025Article
- Risk analysis approaches for microbial ingredients in microbial-based cleaning products.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- Review
- Article
67 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88-99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71-88%. This set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.
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Identifiers
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.