Decoding resistance: interpretable machine learning to predict ciprofloxacin resistance in Shigella spp
This study demonstrates that an interpretable Random Forest machine learning model, trained on k-mer features derived from whole-genome sequencing data of 1,424 Shigella isolates, can accurately and transparently predict ciprofloxacin resistance by integrating both chromosomal mutations and plasmid-mediated determinants.