Deep learning is driving recent advances behind many everyday technologies, including
speech and image recognition, natural language processing and autonomous driving. It is …

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 …

Dimensionality reduction is a common tool for visualization and inference of population
structure from genotypes, but popular methods either return too many dimensions for easy …

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) …

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 …

Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs
of individuals and of the age of genomic variants is key in several population genetic …

Local-ancestry inference (LAI), also referred to as ancestry deconvolution, provides high-
resolution ancestry estimation along the human genome. In both research and industry, LAI …

Accurate inference of population structure is important in many studies of population
genetics. Here we present HaploNet, a method for performing dimensionality reduction and …

Interpreting protein function from sequence data is a fundamental goal of bioinformatics.
However, our current understanding of protein diversity is bottlenecked by the fact that most …