Assessment for antibiotic resistance in <i>Helicobacter pylori</i>: A practical and interpretable machine learning model based on genome-wide genetic variation.

ABSTRACT: Helicobacter pylori (H. pylori) antibiotic resistance poses a global health threat. Accurate identification of antibiotic resistant strains is essential for the control of infection. In the present study, our goal is to leverage the whole-genome data of H. pylori to develop practical and interpretable machine learning (ML) models for comprehensive antibiotic resistance assessment. A total of 296 H. pylori isolates with genome-wide data were downloaded from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) and the National Center for Biotechnology Information (NCBI) databases. By training ML models on feature sets of single nucleotide polymorphisms from SNP calling (SNPs-1), antibiotic-resistance SNP annotated by the Comprehensive Antibiotic Resista

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11934168 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature