Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning

Julian G. Saliba, Wenshu Zheng (), Qingbo Shu, Liqiang Li, Chi Wu, Yi Xie, Christopher J. Lyon, Jiuxin Qu, Hairong Huang, Binwu Ying and Tony Ye Hu ()
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Julian G. Saliba: Tulane University School of Medicine
Wenshu Zheng: Tulane University School of Medicine
Qingbo Shu: Tulane University School of Medicine
Liqiang Li: Shenzhen Third People’s Hospital
Chi Wu: Shenzhen Third People’s Hospital
Yi Xie: Sichuan University
Christopher J. Lyon: Tulane University School of Medicine
Jiuxin Qu: Shenzhen Third People’s Hospital
Hairong Huang: Beijing Chest Hospital of Capital Medical University
Binwu Ying: Sichuan University
Tony Ye Hu: Tulane University School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58214-6

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