D. R. Schrider

This paper introduces IntroUNET, a deep learning method for identifying introgressed alleles using semantic segmentation, showing high accuracy in simulated and real data from Drosophila.

This study uses simulations to show that autopolyploids face greater fitness costs during range expansion than diploids, while allopolyploids with disomic inheritance are shielded, and range expansion accelerates diploidization in autotetraploids.

This paper trains a GAN to generate artificial population genetic alignments that mimic key features of real data across diverse evolutionary models.

The paper identifies sex-differentially variable genes across human tissues and links them to biological functions and disease, suggesting a genetic basis and evolutionary model for sex-specific expression variability.

Timesweeper identifies selective sweeps in population genomic time-series data using a convolutional neural network, showing high accuracy and resolution in detecting selected variants and estimating selection coefficients.

The paper analyzes 155 Drosophila genomes to detect widespread introgression across nine monophyletic radiations, revealing both ancient and recent hybridization events.

The paper investigates introgression in dragonflies and damselflies, showing it is widespread and has shaped their evolutionary history, including deep ancestral introgression between superfamily lineages.

This paper introduces a deep learning method to detect ongoing selective sweeps in mosquito populations, finding strong overrepresentation of sweeps at insecticide resistance loci.

The paper introduces stdpopsim, a community-maintained library for population genetic models, to standardize simulations and facilitate benchmarking of inference tools.

This paper applies convolutional neural networks to population genetic inference, showing they match or exceed current methods in accuracy and model selection.

The paper introduces diploS/HIC, a deep learning method that classifies selective sweeps using unphased genotypes, showing effectiveness at moderate sample sizes.

This paper reviews the application of supervised machine learning in population genetics, highlighting its potential to outperform traditional methods in analyzing genomic data.

This study finds that soft sweeps, not hard sweeps, are the dominant mode of recent human adaptation across six populations.