D. Schrider

Reliable estimation of tree branch lengths using deep neural networks

This study uses deep learning to predict phylogenetic branch lengths, showing improved accuracy and efficiency in challenging regions compared to traditional methods.

Population size rescaling significantly biases outcomes of forward-in-time population genetic simulations

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.

Demographic history and the efficacy of selection in the globally invasive mosquito Aedes aegypti

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.

Background selection does not mimic the patterns of genetic diversity produced by selective sweeps

The paper simulates genetic diversity patterns under background selection and hitchhiking, finding that BGS rarely mimics hitchhiking signatures and does not substantially increase false positives.

Accurate inference of tree topologies from multiple sequence alignments using deep learning

This study uses a deep convolutional neural network to infer quartet phylogenetic topologies from sequence alignments, showing high accuracy and robustness to parameter space biases.

Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia

This paper introduces FILET, a supervised machine learning method to detect introgressed loci between Drosophila species, finding significant introgression from D. simulans to D. sechellia.