Sang‐Hyuk Jung

SingleBrain integrates snRNA-seq and genotype data to map cis-eQTLs across brain cell types, identifying novel disease-associated genes and causal variants.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

The paper introduces cyMAE, a masked autoencoder for automated immunophenotyping, showing it improves accuracy and interpretability over manual methods in cytometry data analysis.

This study performs a GWAS on creativity using 241,736 individuals, identifying 25 loci and showing genetic overlap with several psychiatric disorders.

This paper introduces netCRS, a network-based comorbidity risk score that improves myocardial infarction prediction using PheWAS data, achieving better AUC than traditional PRS methods.

NETMAGE generates interactive phenotype networks from PheWAS data to visualize shared genetic associations and identify pleiotropy, aiding in drug target discovery.