ArticleNature communications2025
Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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Who cites it
5 citing papers in PubMed.
- Emerging insights into CC and CXC chemokines and their receptors in Mycobacterium tuberculosis infection.FEBS open bio · 2026Review
- Artificial Intelligence in the Diagnosis and Management of Pulmonary Tuberculosis: A Review of Current Applications and Future Perspectives.Therapeutics and clinical risk management · 2026Review
- Extracellular Vesicles for Clinical Diagnostics: From Bulk Measurements to Single-Vesicle Analysis.ACS nano · 2025Review
- Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.Nature communications · 2025Article
- Predicting VRE Infection After Liver Transplantation With a Time-Updated Colonization Score.Transplant infectious disease : an official journal of the Transplantation SocietyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-positive cross-resistance artifacts without prior knowledge. GAM analysis of 7,179 Mycobacterium tuberculosis (Mtb) isolates identifies gene targets for all analyzed drugs, revealing comparable performance but fewer cross-resistance artifacts than World Health Organization (WHO) mutation catalogue approach, which requires expert rules and precedents. GAM also reveals generalizability, demonstrating high predictive accuracy with 3,942 S. aureus isolates. GAM refinement by machine learning (ML) improves predictive accuracy with small or incomplete datasets. These findings were validated using 427 Mtb isolates from three sites, where GAM inputs are also found to be more suitable in ML prediction models than WHO inputs. GAM + ML could thus address the limitations of current drug resistance prediction methods to improve treatment decisions for drug-resistant microbial infections.
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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.