Brian Clarke

Time-resolved single-cell screening reveals the dynamic regulatory architecture of human cardiogenesis

This study uses time-resolved single-cell CRISPR screening and a multiome atlas to uncover dynamic gene regulatory networks in human cardiogenesis, identifying stage-specific regulators and CHD-linked gene functions.

Multimodal phenotyping defines variant-to-function maps for RBM20 in dilated cardiomyopathy

The paper develops a deep mutational scanning framework to map variants to molecular functions in RBM20, identifying functional hotspots and therapeutic strategies for dilated cardiomyopathy.

Mapping trans-eQTLs at single-cell resolution using Latent Interaction Variational Inference.

This paper introduces LIVI, a deep learning framework for mapping trans-eQTLs at single-cell resolution, and shows it detects more trans-eQTLs than existing methods and identifies cell types and genes involved in autoimmune disease risk.

Predicting functional constraints across evolutionary timescales with phylogeny-informed genomic language models

This paper introduces GPN-Star, a phylogeny-informed genomic language model that improves variant effect prediction across species and evolutionary timescales.

Model-based inference of RNA velocity modules improves cell fate prediction

The paper introduces cell2fate, a model-based RNA velocity method that improves cell fate prediction by decomposing velocity solutions into modules and applying it to human brain development data.

Integration of variant annotations using deep set networks boosts rare variant association genetics

This paper introduces DeepRVAT, a deep learning model for rare variant association genetics, showing improved gene discovery and risk prediction using whole-exome sequencing data from UK Biobank.