Alexandre Drouin – Research Scientist at Element AI

Genotype-to-phenotype prediction of antimicrobial resistance

Abstract: Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome sequences shows great promise in promoting better use of antimicrobial agents. Machine learning is a tool of choice for this task, but the scarceness and high dimensionality of the data make for a challenging problem. In this talk, I will outline recent work on using machine learning to predict categorical (resistant vs. susceptible) and quantitative (minimum inhibitory concentrations) phenotypes of antimicrobial resistance. Special emphasis will be given to rule-based algorithms that produce interpretable models (https://github.com/aldro61/kover/).


Assya Trofimov – PhD Student at IRIC

Title: TBD

Abstract: TBD


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