Machine Learning for antimicrobial resistance (AMR) prediction

Ulysse Guyet will be giving a talk titled "ARSENAL: Antimicrobial ReSistance prEdictioN by mAchine Learning approach" at the ML Microbial Genomics workshop on Friday 23/09.

We developed a machine learning method - ARSENAL - for predicting the minimum inhibitory concentration (MIC) of several antibiotics based on genomic data. ARSENAL relies on one hand on the sequence (k-mers), and on the other hand on the genome structure (gene composition) and the gene orthology links between the strains of the same species. Functional interpretation of the most predictive features confirmed the biological relevance of the ARSENAL model when applied to Streptococcus pneumoniae.