In population genetics, the emergence of large-scale genomic data for various species and
populations has provided new opportunities to understand the evolutionary forces that drive …

Following the widespread use of deep learning for genomics, deep generative modeling is
also becoming a viable methodology for the broad field. Deep generative models (DGMs) …

Data shift is a phenomenon present in many real-world applications, and while there are
multiple methods attempting to detect shifts, the task of localizing and correcting the features …

Motivation Local ancestry inference (LAI) is the high resolution prediction of ancestry labels
along a DNA sequence. LAI is important in the study of human history and migrations, and it …

Applications of generative models for genomic data have gained significant momentum in
the past few years, with scopes ranging from data characterization to generation of genomic …

Motivation Modern biobanks provide numerous high-resolution genomic sequences of
diverse populations. These datasets enable a better understanding of genotype-phenotype …

Synthetic data generation via generative modeling has recently become a prominent
research field in genomics, with applications ranging from functional sequence design to …

Adversarial attacks can drastically change the output of a method by small alterations to its
input. While this can be a useful framework to analyze worst-case robustness, it can also be …

In this paper, we introduce the first diffusion model designed to generate complete synthetic
human genotypes, which, by standard protocols, one can straightforwardly expand into full …

Large-scale and increasingly diverse datasets power modern genomic studies, yet robust
data integration and homogenization across varying sources remains a challenge. The …