GT LEGO Machine Learning pour la génomique - Automne 2024
9-9 déc. 2024 PARIS (France)
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Programme
Semaine
Lun. 09
Liste
Lun. 09
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
Petit déjeuner (SCAI)
8:30 - 9:00 (30min)
Petit déjeuner (SCAI)
Introduction
9:00 - 9:15 (15min)
Introduction
Tree-based variational inference for Poisson log-normal models: application to the gut microbiome
9:15 - 9:45 (30min)
Tree-based variational inference for Poisson log-normal models: application to the gut microbiome
Alexandre Chaussard
Keynote: IA, tests diagnostiques et cancer
9:45 - 10:30 (45min)
Keynote: IA, tests diagnostiques et cancer
Michael Blum
Pause café
10:30 - 11:00 (30min)
Pause café
Self-supervised representation learning on gene expression data for phenotype prediction
11:00 - 11:30 (30min)
Self-supervised representation learning on gene expression data for phenotype prediction
Kevin Dradjat
Learning Single-cell Drug Responses Using Differential Autoencoder Model
11:30 - 12:00 (30min)
Learning Single-cell Drug Responses Using Differential Autoencoder Model
Wang Shuhui
scPRINT: A transcriptomic foundation model for inferring molecular interactions
12:00 - 12:30 (30min)
scPRINT: A transcriptomic foundation model for inferring molecular interactions
Jérémie Kalfon
Déjeuner et Posters
12:30 - 14:30 (2h)
Déjeuner et Posters
Keynote: Apprentissage profond pour l'inférence phylogénétique de diversification d'espèces
14:30 - 15:15 (45min)
Keynote: Apprentissage profond pour l'inférence phylogénétique de diversification d'espèces
Hélène Morlon
Transformers for EpiDemiological DYnamics: from genomic data to epidemiological parameters
15:15 - 15:45 (30min)
Transformers for EpiDemiological DYnamics: from genomic data to epidemiological parameters
Vincent Garot
Pause café
15:45 - 16:15 (30min)
Pause café
ProtMamba: a homology-aware but alignment-free protein state space model
16:15 - 16:45 (30min)
ProtMamba: a homology-aware but alignment-free protein state space model
Cyril Malbranke
Expanding the space of self-reproducing RNA using generative probabilistic models
16:45 - 17:15 (30min)
Expanding the space of self-reproducing RNA using generative probabilistic models
Martin Weigt
Conclusion
17:15 - 17:30 (15min)
Conclusion
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