PanARGMiner (Pan-Genomic Antimicrobial Resistance Gene Miner): An advanced feature selection framework for extracting...

Identifying antimicrobial resistance (AMR)-related biomarkers from large-scale genomic datasets is often akin to finding a needle in a haystack. With pan-genomic data containing more than 100,000 gene sequences, isolating features that truly drive resistance remains a major challenge in computational biology. Here we present PanARGMiner, a machine learning-based feature selection framework designed to robustly extract highly relevant and informative biomarkers from high-dimensional biological data. PanARGMiner uses an ensemble-based feature selection strategy to select highly informative and compact feature subsets. It then utilizes repeated iterations to ensure the stability and reliability of the proposed framework, enabling PanARGMiner to generate significantly reduced features with comparable prediction performance compared to those obtained with other feature selection algorithms. Applying PanARGMiner to bacterial pan-genomic antimicrobial resistance datasets successfully extracted as few as one to ten candidate AMR biomarkers from datasets with more than 100,000 genes for five common pathogens. Although many of the extracted candidate AMR biomarkers are well-known resistance genes, proteins not known to be associated with AMR mechanisms, including functionally uncharacterized hypothetical proteins, were also extracted. This indicates the potential of PanARGMiner in revealing both established and novel mechanisms of antibiotic resistance, thus providing actionable insights for biomarker discovery, functional genomics, and precision medicine based on complex data. Its ability to uncover both known and uncharacterized resistance-related features offers new opportunities for research and clinical applications in combating AMR.