ArticleJournal of bacteriology2014
Genome sequence-based discriminator for vancomycin-intermediate Staphylococcus aureus.
Article in Journal of bacteriology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed, 36 citations in OpenAlex.
- Artificial Intelligence in Rapid Diagnostics for Antimicrobial Resistance: From Bench to Bedside.Health science reports · 2026Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation.Antibiotics (Basel, Switzerland) · 2023Review
- Vancomycin drug resistance, an emerging threat to animal and public health.Frontiers in veterinary science · 2022Review
- A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.The Journal of antibiotics · 2021Review
- Using genomics to understand meticillin- and vancomycin-resistantMicrobial genomics · 2020Review
- Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection.BMC bioinformatics · 2019Article
- Sequencing-based methods and resources to study antimicrobial resistance.Nature reviews. Genetics · 2019Review
- Interpretable genotype-to-phenotype classifiers with performance guarantees.Scientific reports · 2019Article
- Integrated analysis of population genomics, transcriptomics and virulence provides novel insights into Streptococcus pyogenes pathogenesis.Nature genetics · 2019Article
- Association between cell growth and vancomycin resistance in clinical community-associated methicillin-resistantInfection and drug resistance · 2019Article
- Rapid Detection of Heterogeneous Vancomycin-IntermediateFrontiers in microbiology · 2018Article
- Rapid Detection of Vancomycin-Intermediate Staphylococcus aureus by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry.Journal of clinical microbiology · 2016Article
- Machine Learning and Decision Support in Critical Care.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2016Article
- Dissecting vancomycin-intermediate resistance in staphylococcus aureus using genome-wide association.Genome biology and evolution · 2014Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Vancomycin is the mainstay of treatment for patients with Staphylococcus aureus infections, and reduced susceptibility to vancomycin is becoming increasingly common. Accordingly, the development of rapid and accurate assays for the diagnosis of vancomycin-intermediate S. aureus (VISA) will be critical. We developed and applied a genome-based machine-learning approach for discrimination between VISA and vancomycin-susceptible S. aureus (VSSA) using 25 whole-genome sequences. The resulting machine-learning model, based on 14 gene parameters, including 3 molecular typing markers and 11 genes implicated in reduced vancomycin susceptibility, is able to unambiguously distinguish between the VISA and VSSA isolates analyzed here despite the fact that they do not form evolutionarily distinct groups. As such, the model is able to discriminate based on specific genomic markers of antibiotic susceptibility rather than overall sequence relatedness. Subsequent evaluation of the model using leave-one-out validation yielded a classification accuracy of 84%. The machine-learning approach described here provides a generalized framework for the application of genome sequence analysis to the classification of bacteria that differ with respect to clinically relevant phenotypes and should be particularly useful in defining the genomic features that underlie antibiotic resistance.
Indexed as
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.