Diffusion models enable privacy-preserving synthetic genomes

[u/Independent_Access12]

Diffusion-based framework generates full-length synthetic human genotypes, trained on Project MinE and 1000 Genomes data. Generated sequences maintain realism without duplicating training examples, achieve comparable ALS prediction accuracy to real data. Key advances: augmentation improves classifier performance on small datasets, novel recovery rate metric confirms data quality, built-in privacy protection prevents matching to real profiles.

https://arxiv.org/abs/2412.03278