ArticleJournal of clinical microbiology2023
Machine-Learning Model for Prediction of Cefepime Susceptibility in Escherichia coli from Whole-Genome Sequencing Data.
Article in Journal of clinical microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Pooled it
- Pooled it
- Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.npj antimicrobials and resistance · 2026Article
- Evaluation of a machine learning system for genomic antimicrobial susceptibility determination on a clinically representative test set.Microbiology spectrum · 2026Article
- Comparative Evaluation of Sequencing Technologies for Detecting Antimicrobial Resistance in Bloodstream Infections.Antibiotics (Basel, Switzerland) · 2025Review
- Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions forMicrobiology spectrum · 2025Article
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 2025Article
- Streamlining whole genome sequencing for clinical diagnostics with ONT technology.Scientific reports · 2025Article
- Advancements in AI-driven drug sensitivity testing research.Frontiers in cellular and infection microbiology · 2025Review
- Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistantAntimicrobial agents and chemotherapy · 2024Article
- Analytical and clinical validation of direct detection of antimicrobial resistance markers by plasma microbial cell-free DNA sequencing.Journal of clinical microbiology · 2024Article
- Status check: next-generation sequencing for infectious-disease diagnostics.The Journal of clinical investigation · 2024Article
- The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology.Journal of clinical microbiology · 2023Review
- Multicenter Evaluation of the BIOFIRE Blood Culture Identification 2 Panel for Detection of Bacteria, Yeasts, and Antimicrobial Resistance Genes in Positive Blood Culture Samples.Journal of clinical microbiology · 2023Article
- Machine learning and feature extraction for rapid antimicrobial resistance prediction ofFrontiers in microbiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The declining cost of performing bacterial whole-genome sequencing (WGS) coupled with the availability of large libraries of sequence data for well-characterized isolates have enabled the application of machine-learning (ML) methods to the development of nonlinear sequence-based predictive models. We tested the ML-based model developed by Next Gen Diagnostics for prediction of cefepime phenotypic susceptibility results in Escherichia coli. A cohort of 100 isolates of E. coli recovered from urine (
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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.