Cyril Furtlehner

Diffusion-based artificial genomes and their usefulness for local ancestry inference

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

Latent generative modeling of long genetic sequences with GANs

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

Deep convolutional and conditional neural networks for large-scale genomic data generation

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

Creating Artificial Human Genomes Using Generative Models

This study uses generative models to create artificial human genomes that can improve genetic research without compromising privacy.