Machine Learning Approaches for Predicting Antimicrobial Resistance (AMR)

Antimicrobial resistance is a growing global threat, driven by rapid evolution of microbes and increased horizontal gene transfer. Traditional laboratory-based AMR testing is slow and resource-intensive, creating a need for computational tools that can rapidly predict resistance patterns. Machine learning provides powerful approaches to detect resistance markers, classify microbial strains, and predict phenotypic resistance using genomic and metagenomic features.

This workshop blends microbiology and AI, teaching participants how to preprocess AMR datasets, extract meaningful features, train ML models, and validate predictive performance. Real-world case studies will highlight applications of ML in clinical diagnostics, public health surveillance, environmental AMR monitoring, and drug discovery. Participants will learn to integrate ML pipelines with well-known AMR databases and interpret model outputs for actionable insights.