ReviewJournal of clinical microbiology2019
Genome-Based Prediction of Bacterial Antibiotic Resistance.
Review in Journal of clinical microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 187 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
187 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review.The Journal of antimicrobial chemotherapy · 2020Pooled it
- Artificial Intelligence in Rapid Diagnostics for Antimicrobial Resistance: From Bench to Bedside.Health science reports · 2026Article
- Global Research Trends and Insights on AI Usage in Tuberculosis: Bibliometric Analysis.JMIR medical informatics · 2026Article
- Antibiotic-Specific Genotype-Phenotype Concordance and Cross-Database Interoperability in PublicInternational journal of molecular sciences · 2026Article
- Beyond the Usual Suspects: EmergingInternational journal of molecular sciences · 2026Review
- Rapid and modular workflows for same-day sequencing-based detection of bloodstream infections and antimicrobial resistance determinants using culture-enriched samples.Microbiology spectrum · 2026Article
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response.Nature communications · 2026Article
- Rapid Molecular Diagnostics for Bloodstream Infection in Patients with Chronic Kidney Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Phenotype-Genotype Discordance in Antimicrobial Resistance ofPathogens (Basel, Switzerland) · 2026Review
- A Risk Model Incorporating the Novel Inflammatory Biomarker CD64 for Predicting Bloodstream Infection in Suspected Cases.Antibiotics (Basel, Switzerland) · 2026Article
- [Clinical and molecular characteristics of neonatalZhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Article
- Putative azithromycin resistance mutations inAntimicrobial agents and chemotherapy · 2026Article
- Multi task learning based early prediction model for antibiotic resistance using multi institutional cohort data.Scientific reports · 2026Article
- Complementary mNGS and traditional testing for bloodstream infections.Open medicine (Warsaw, Poland) · 2026Review
- Laboratory tests for bovine respiratory bacteria and antimicrobial resistance in commercial feedlot cattle: comparing culture, long-read metagenomics, and recombinase polymerase amplification.Frontiers in microbiology · 2026Article
- Phage endolysins as alternative antimicrobials: mechanisms, clinical progress, and emerging resistance frameworks.Frontiers in microbiology · 2026Review
- stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents.Bioinformatics advances · 2026Article
- Harnessing interpretable deep learning to predict resistance inFrontiers in cellular and infection microbiology · 2026Article
127 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
3 authors.
Funding
Abstract
Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.
Indexed as
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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.