Guillaume Charpiat

This paper shows that ignoring gene-gene interactions in GWAS can lead to false positive results, highlighting the need for more complex models.

Attentive-SPIDNA uses attention mechanisms to infer population size history from genomic data, outperforming existing methods and handling varying haplotype numbers.

This study introduces a frugal diffusion model for generating artificial genomes and evaluates its utility for local ancestry inference using synthetic and real data.

The paper generates synthetic genomes using PCA and GANs, showing improved population structure and privacy metrics compared to existing methods.

This paper introduces convolutional and conditional neural networks for generating large-scale artificial genomes with high SNP numbers while minimizing privacy leakage.