ArticleTrends in microbiology2025
Unlocking antimicrobial resistance with multiomics and machine learning.
Article in Trends in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Conserved protein sequence-structure signatures identify antibiotic resistance genes from the human microbiome.Microbiome · 2026Article
- Article
- Comparative Biofilmomics of Antimicrobial-ResistantAnimals : an open access journal from MDPI · 2026Review
- Genotype-phenotype modeling of light ecotypes inbioRxiv : the preprint server for biology · 2026Article
- Emerging synthetic biology-assisted technologies for overcoming antibiotic resistance: CRISPR-Cas, bacteriophage, microbiome, and metabolic engineering-based solutions.Journal of microbiology (Seoul, Korea) · 2026Review
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- Review
- Bacterial Proteomics and Antibiotic Resistance Identification: Is Single-Cell Analysis a Worthwhile Pursuit?Pathogens (Basel, Switzerland) · 2025Review
- From sequence to signature: Machine learning uncovers multiscale feature landscapes that predict AMR across ESKAPE pathogens.bioRxiv : the preprint server for biology · 2025Article
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.
Funding
Abstract
The global antimicrobial resistance (AMR) emergency is driven by complex and evolving molecular mechanisms. Cutting-edge machine learning methods and multiomics technologies can help to combat this crisis by predicting novel AMR biomarkers and outcomes with unprecedented precision and speed, offering critical insights into the molecular underpinnings of AMR.
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.