Identifying Key Metadata Predictors of Salmonella AMR Genotypes through Machine Learning

MADA Course Project | University of Georgia, Department of Poultry Science
👤 Marco Reina • 📅 Spring 2026

Web manuscript available here

Project Overview

This project applies machine learning techniques to identify key metadata predictors associated with antimicrobial resistance (AMR) genotypes in Salmonella isolates from poultry sources. Understanding these predictors supports food safety surveillance and targeted intervention strategies in poultry production systems.

Research Question

1. Which metadata features (e.g., source, geography, year, serotype) best predict AMR genotypes in Salmonella?

Contributing & Peer Reviews

This project was developed as part of the MADA course with peer feedback:
- Riley Herber
- Elle Adams
- Sarra Aljawad