October, Wednesday 18th

14:30 (room 2014, building 660) (see location):

Théophile Sanchez

(TAU team)

Title: End-to-end Deep Learning Approach for Demographic History Inference


Recent methods for demographic history inference have achieved good
results, circumventing the complexity of raw genomic data by summarizing
them into handcrafted features called summary statistics. We developed a
new approach based on deep learning that takes as input the variant
sites found within a sample of individuals from the same population, and
infers demographic descriptor values without relying on these predefined
summary statistics. By letting our model choose how to handle raw data
and learn its own way to embed them, we were able to outperform a method
frequently used in population genetics for the inference of three out of
seven demographic descriptor values of a scenario with a bottleneck and
two expansions. This is still a preliminary work and we are hopeful that
future developments would allow us to tackle a broader range of
demographic scenarios and outperform previous methods by developing more
flexible artificial neural network architectures.

Contact: guillaume.charpiat at

Collaborateur(s) de cette page: guillaume .
Page dernièrement modifiée le Mercredi 25 octobre 2017 12:51:15 CEST par guillaume.