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

Théophile Sanchez 1 Guillaume Charpiat 1 Flora Jay 2, 3, 1
1 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
2 BioInfo - LRI - Bioinformatique (LRI)
LRI - Laboratoire de Recherche en Informatique
Abstract : Recent methods for demographic history inference have achieved good results, avoiding the complexity of raw genomic data by summarizing them into handcrafted features called expert statistics. Here we introduce a new approach 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 expert 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 by geneticists for the inference of two demographic descriptor values while using less data.
Type de document :
Communication dans un congrès
Paris-Saclay Junior Conference on Data Science and Engineering, Sep 2017, Orsay, France
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https://hal.archives-ouvertes.fr/hal-01679385
Contributeur : Flora Jay <>
Soumis le : mardi 9 janvier 2018 - 19:19:21
Dernière modification le : mardi 8 janvier 2019 - 08:36:01

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  • HAL Id : hal-01679385, version 1

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Théophile Sanchez, Guillaume Charpiat, Flora Jay. SPI-DNA: End-to-end Deep Learning Approach for Demographic History Inference. Paris-Saclay Junior Conference on Data Science and Engineering, Sep 2017, Orsay, France. 〈hal-01679385〉

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