Machine learning applications in genomics and proteomics for disrupting antimicrobial resistance

Antimicrobial resistance (AMR) poses a significant global health challenge, underscoring the need for faster and more accurate diagnostic tests. Among emerging approaches, machine learning applied to omics data, particularly whole genome sequencing and MALDI-TOF MS, has gained increasing attention. In this PhD project, I leveraged recent machine learning advances to address critical issues, including rapid AMR detection, resistance mechanism characterisation, and ultra-fast bacterial strain typing.