ArticleFrontiers in microbiology2026
Utilizing whole genome sequencing data for machine learning driven prediction of antibiotic resistance in
Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Antibiotic-Specific Genotype-Phenotype Concordance and Cross-Database Interoperability in PublicInternational journal of molecular sciences · 2026Article
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7 authors.
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Abstract
Background: Antimicrobial resistance (AMR) is an escalating global public health threat that substantially undermines the effectiveness of standard anti-infective therapies and increases the risk of adverse clinical outcomes. Methods: We analyzed whole-genome sequencing (WGS) data from 1952 Results: Across all antibiotics and feature representations, model AUCs ranged from 0.6691 to 0.9879. Gene-based and integrated-feature models showed superior and stable performance (AUC 0.8936-0.9787 and 0.8888-0.9879, respectively) compared with SNP-based models and k-mer models. Prediction performance was near-saturated for aminoglycosides (GEN and TOB) and ciprofloxacin (CIP), whereas β-lactams exhibited greater heterogeneity, with amoxicillin/clavulanate (AMX/CLA) exhibiting the lowest AUC. Feature-importance analysis highlighted 40 core genes that were highly concordant with established resistance mechanisms, including Conclusion: This study demonstrated that machine learning models built on WGS data can accurately and efficiently predict resistance phenotypes to 10 commonly used antibiotics in
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