Sion Bayliss

Machine learning to predict the source of campylobacteriosis using whole genome data

This paper uses machine learning on whole genome data to improve source attribution of Campylobacter jejuni infections, achieving higher accuracy than traditional methods.

Quantifying bacterial evolution in the wild: a birthday problem for Campylobacter lineages

The paper estimates molecular clock rates in Campylobacter using a birthday problem analogy to quantify lineage turnover and diversification in environmental bacteria.

PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria

PIRATE clusters and classifies orthologous gene families in bacterial pangenomes across sequence similarity thresholds and scales efficiently with large datasets.

Piggy: A Rapid, Large-Scale Pan-Genome Analysis Tool for Intergenic Regions in Bacteria

Piggy analyzes intergenic regions in bacterial pan-genomes, showing divergent IGRs correlate with gene expression differences in Staphylococcus aureus.