Boyoung Yoo

AIMHIGH identifies novel disease-causing genes by analyzing inbred mouse strain data and machine learning-driven literature-based discovery.

This study evaluates the impact of population structure on murine GWAS and finds minimal effects in most datasets, with some cases where PS correction may reject true signals.

InpherNet improves disease gene hypothesis generation by leveraging gene network neighbors, achieving higher accuracy than existing methods in prioritizing causative genes.

The paper analyzes structural variation in inbred mouse strains to identify genetic factors associated with autism-related traits.

The paper identifies transcriptional networks in developing mouse projection neurons by correlating chromatin accessibility with gene expression to understand cell fate diversity.