Conference Program

Tentative overview

Time September 23rd 2022 Title
10:15 - 10:20 Welcome
10:20 - 11:00 First keynote: Alice McHardy Computational inference of microbial genotype-phenotype relationships
11:00 - 11:20 Talk #1: Gabriel Carvalho, Jean-Philippe Rasigade, Katy Jeannot, Patrick Plésiat, Richard Bonnet, Laurent Dortet and François Vandenesch Predicting antimicrobial resistance genes from phenotypic resistance profiles: a proof-of-concept study
11:20 - 11:40 Talk #2: Ulysse Guyet, Léa Bientz, Véronique Dubois, Jacques Corbeil, Jie Feng, Alexis Groppi and Macha Nikolski ARSENAL: Antimicrobial ReSistance prEdictioN by mAchine Learning approach
11:40 - 12:00 Talk #3: Niklas Stotzem, Fernando Guntoro and Leonid Chindelevitch BenchmarkDR: A modular and expandable benchmarking pipeline for machine learning based antimicrobial resistance prediction
12:00 - 12:15 Break
12:15 - 12:55 Second keynote: Nicole Wheeler Machine learning for predicting phenotype from genotype: how well do algorithms capture causal mechanisms?
12:55 - 13:15 Talk #4: Jean Cury, Théophile Sanchez, Erik Bray, Jazeps Medina-Tretmanis, Maria Avila-Arcos, Emilia Huerta-Sanchez, Guillaume Charpiat and Flora Jay Inferring effective population sizes of bacterial populations while accounting for unknown recombination and selection: a deep learning approach
13:15 - 13:35 Talk #5: Sion Bayliss, Rebecca Locke, Claire Jenkins, Marie-Anne Chattaway, Timothy Dallman and Lauren Cowley Hierarchical machine learning predicts geographical origin of Salmonella within four minutes of sequencing
13:35 - 13:45 Conclusion