Prediction of Antibiotic Resistance Phenotypes and Minimum Inhibitory Concentrations in Salmonella Using Machine Learning Analysis of Its Pan-Genome and Pan-Resistome Features.

Abstract excerpt

Traditional experimental methods for determining antibiotic resistance phenotypes (ARPs) and minimum inhibitory concentrations (MICs) in bacteria are laborious and time consuming. This study aims to explore the potential of whole-genome sequencing data combined with machine learning models for robustly predicting ARPs and MICs in Salmonella. Using a training set of 6394 Salmonella genomes alongside antimicrobial...

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• Drug Resistance, Bacterial
• Genome, Bacterial
• Machine Learning
• Microbial Sensitivity Tests
• Prediction Algorithms