Fiche de QUEYREL Maxence paul vincent

Titre : End-to-End Deep Learning and Subgroup discovery approaches to learn from metagenomics data
Date : 13 octobre 2021
Heure: 09:00
Résumé : Over the past decade, technological advances have made high-speed, high-resolution sequencing of genetic material possible at ever lower cost (from millions to one hundred dollars). In this context, the human microbiome has demonstrated its great capacity to stratify and classify various human diseases, and is increasingly considered as our second "genome". There remain many challenges associated with mainstream metagenomics pipelines that are both time consuming and not stand-alone. In this thesis, we address this issue by training deep neural networks directly from raw sequencing data building an embedding of metagenomes called Metagenome2Vec. We also explore subgroup discovery algorithms that we adapt to build a classifier with a reject option which then delegates samples, not belonging to any subgroup, to a supervised algorithm.
Lieu : salle 24.34.212, Campus Jussieu