Joshua M Popp

The paper develops a graph neural network framework that integrates biological knowledge to predict phenotypes from proteomics data, outperforming linear models and other architectures.

The paper identifies response eQTLs associated with environmental exposures in human cells and finds they overlap with disease-associated loci not explained by steady-state eQTLs.

The paper identifies expression quantitative trait loci in 29 cell-types from heterogeneous differentiating cultures and characterizes regulatory dynamics across differentiation trajectories.

DAESC is a statistical method for differential allele-specific expression analysis in scRNA-seq data, identifying genes with context-specific regulatory effects in endoderm differentiation and type 2 diabetes.

This paper introduces SURGE, a method to discover context-specific eQTLs from single-cell RNA-seq data, showing its relevance in disease through colocalization analysis.

The paper identifies lineage-specific dynamic eQTLs during human cardiomyocyte differentiation using single-cell RNA-seq and deconvolves bulk RNA-seq data to attribute dynamic effects to specific lineages.

The paper uses human embryoid bodies to study inter-individual regulatory differences across diverse cell types using single-cell RNA-sequencing.