Antimicrobial resistance (AMR) is one of the leading global health threats. In Klebsiella pneumoniae Species Complex (KpSC), resistance is driven not just by gene presence but by how many copies of a resistance gene a bacterium carries — amplification of chromosomal genes and variable plasmid copy number (PCN) both matter clinically. CNVRock adapts the autoresearch strategy — where an AI agent continuously proposes and runs ML experiments overnight — to detect AMR-related copy-number variation in KpSC whole-genome sequencing data. A convolutional VAE learns a low-dimensional representation of genome-wide read depth; a Gaussian HMM segments the latent trajectories into copy-number states; a gene caller converts those states into per-gene calls. Claude proposes the next experiment, emails a summary, and a background daemon runs it after authorisation.

Alternative Title

CNVRock — AMR gene copy-number variation in Klebsiella pneumoniae using variational autoencoders

Keywords

Antimicrobial resistance; Klebsiella pneumoniae; Artificial intelligence