Machine Learning Approaches for Predicting Antimicrobial Resistance from Genomic Data: A Systematic Review

Abstract

Antimicrobial resistance (AMR) represents a major global health challenge, significantly limiting
the effective prevention and treatment of bacterial infections. Advances in whole-genome
sequencing (WGS) have enabled rapid identification of genetic determinants associated with
resistance. However, conventional rule-based approaches primarily rely on previously
characterized resistance genes and mutations, restricting their ability to detect novel or complex
resistance mechanisms. In this context, machine learning (ML) has emerged as a powerful
alternative, leveraging high-dimensional genomic datasets to model complex and nonlinear
relationships between genotype and phenotype. Over the past decade, ML-based approaches have
been increasingly applied to predict antibiotic susceptibility directly from genomic information in
clinically important pathogens such as Mycobacterium tuberculosis, Escherichia coli,
Staphylococcus aureus, Klebsiella pneumoniae, and Salmonella species. This systematic review
summarizes recent advancements in genomic feature extraction, algorithm development, model
validation strategies, and the clinical applicability of ML-driven AMR prediction tools. Various
computational frameworks, including supervised, unsupervised, and deep learning models, are
critically examined alongside benchmarking practices, validation methodologies, and model
interpretability techniques. Emerging directions such as explainable artificial intelligence,
federated learning, and multi-omics integration are also discussed for their potential to enhance
predictive performance and clinical utility. Despite promising results in controlled settings,
several challenges remain, including data heterogeneity, limited generalizability, regulatory
constraints, and ethical considerations. Overall, this review provides a comprehensive perspective
on the current state and future prospects of ML-based genomic diagnostics for antimicrobial
resistance.