Daniel R. Schrider

The paper uses long-read sequencing to identify structural variants in Culex quinquefasciatus, revealing their potential role in insecticide resistance and adaptation.

This study investigates how convolutional neural networks in population genetics implicitly compute summary statistics and reveals their ability to efficiently approximate long-range linkage disequilibrium.

This paper develops an evolutionary model to evaluate extinction therapy strategies for cancer, showing it outperforms combination therapy when cross-resistance exists.

This study shows that gene conversion can create the appearance of soft sweeps even when selection acts on single-origin mutations, challenging previous assumptions about the prevalence of soft sweeps.

This study introduces TEforest, a machine learning method that improves short-read TE detection and genotyping by leveraging long-read assemblies, achieving better accuracy than traditional methods.

The paper uses machine learning to detect soft and hard selective sweeps in Aedes aegypti, finding soft sweeps are more common, suggesting rapid adaptation to environmental pressures.

The paper simulates evolution in diploid and autotetraploid populations to show that pseudo-overdominance is more common in polyploids due to reduced recombination and increased masking of deleterious mutations.

This study examines transposable element insertions in Aedes aegypti genomes to assess their contribution to genetic diversity and potential role in insecticide resistance.

The paper extends the stdpopsim simulation framework to include realistic selection models, enabling studies of selection's impact on genetic variation across species.

This study analyzes the demographic history and genetic diversity of Aedes aegypti across continents, finding reduced genetic diversity and minor selection efficacy reduction in the Americas.

This study shows that rescaling population sizes in forward-in-time simulations introduces significant biases in key outcomes, even at small scaling factors, and suggests that small-scale simulations can help quantify these biases.

The paper proposes a graph convolutional network to directly learn from tree sequences for population genetic inference, showing comparable or better performance than CNNs on benchmark tasks.

The paper identifies four SNPs in the LILRB3 gene associated with graft failure in African American kidney transplant recipients and links them to immune-related diseases.

The paper identifies strong selective sweeps in Aedes aegypti genomes linked to insecticide resistance, showing rapid evolutionary adaptation to vector control.

The paper develops a deep learning object detection method to identify post-admixture adaptive variants by analyzing ancestry-painted genomes, achieving more precise localization than existing methods.

The paper expands the stdpopsim species catalog and improves genome simulations by adding non-crossover recombination and species-specific annotations, making simulations more realistic for non-model organisms.

The paper analyzes genome variation in 1,142 Anopheles mosquitoes to study population structure and insecticide resistance.

The paper sequences genomes of 765 malaria vector mosquitoes to study genetic diversity and insecticide resistance.