David H Rich

The paper presents a deep learning approach that uses phylogenetic dynamic program outputs to predict edges in maximum parsimony trees, achieving high accuracy on simulated and empirical datasets.

This study uses deep learning to detect entrenchment in antibody germline genes, revealing epistatic constraints from both intra-chain and genetically uncoupled partners.

This paper introduces a model that separates mutation and selection processes in antibody language models to improve prediction of mutation effects on antibody function.

The paper introduces larch, a tool that explores the space of optimal phylogenetic trees to find more parsimonious trees and increase diversity in the set of optimal solutions.