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<p>The global threat of infectious diseases is being exacerbated by the issue of antimicrobial resistance (AMR). Drug resistance is growing, thus investigating antibiotic resistance strains in <i>E. coli</i> and urine tract infections (UTIs) is essential for effective diagnosis. Although next-generation sequencing (NGS) has made whole genome sequencing (WGS) widely available and used in AMR studies, it is still challenging to identify bacterial strain that are resistant using sequencing data. Rapid strain detection by deep learning (DL) in combating infections has reduced fatality rates. Introducing ARGai 3.0, a fusion of artificial intelligence (AI) models for resistant <i>E. coli</i> strain detection that is both generalized and streamlined. The ARGAi 3.0 framework for antibiotic-resistant strain classification represents a developing integration of quality control with AI models. Several classification metrics, including sensitivity, specificity, accuracy, precision, ROC-AUC, and F1-score, were assessed using ARGai 3.0 through the use of cross-validation. We hypothesized that, in comparison with baseline AI models, ARGai 3.0 would have superior capability in identifying strains of antibiotic resistance. In comparison with traditional AI models, our ARGai 3.0 demonstrates a 10% enhancement in classification accuracy and is capable of processing complex NGS WGS data. The improved sensitivity, specificity, and minimal computational time of ARGai 3.0 boost its robustness. The integration of machine learning, DL, and NGS data in ARGai 3.0 positions it as a promising tool for future antibiotic resistance surveillance. Due to its generalizability, ARGai 3.0 serves as an effective pipeline for AMR analysis.</p>

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ARGai 3.0: AI fusion approach to identify antibiotic-resistant strains in E. coli using high-throughput whole genome sequencing data

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Debasish Swapnesh Kumar Nayak,
  • Swati Sucharita,
  • Subhra Mohanty,
  • Sabyasachi Mohanty,
  • Shubha Dattatreya Bahinipati,
  • Santanu Kumar Sahoo,
  • Tripti Swarnkar

摘要

The global threat of infectious diseases is being exacerbated by the issue of antimicrobial resistance (AMR). Drug resistance is growing, thus investigating antibiotic resistance strains in E. coli and urine tract infections (UTIs) is essential for effective diagnosis. Although next-generation sequencing (NGS) has made whole genome sequencing (WGS) widely available and used in AMR studies, it is still challenging to identify bacterial strain that are resistant using sequencing data. Rapid strain detection by deep learning (DL) in combating infections has reduced fatality rates. Introducing ARGai 3.0, a fusion of artificial intelligence (AI) models for resistant E. coli strain detection that is both generalized and streamlined. The ARGAi 3.0 framework for antibiotic-resistant strain classification represents a developing integration of quality control with AI models. Several classification metrics, including sensitivity, specificity, accuracy, precision, ROC-AUC, and F1-score, were assessed using ARGai 3.0 through the use of cross-validation. We hypothesized that, in comparison with baseline AI models, ARGai 3.0 would have superior capability in identifying strains of antibiotic resistance. In comparison with traditional AI models, our ARGai 3.0 demonstrates a 10% enhancement in classification accuracy and is capable of processing complex NGS WGS data. The improved sensitivity, specificity, and minimal computational time of ARGai 3.0 boost its robustness. The integration of machine learning, DL, and NGS data in ARGai 3.0 positions it as a promising tool for future antibiotic resistance surveillance. Due to its generalizability, ARGai 3.0 serves as an effective pipeline for AMR analysis.