SNU GSDS Lee Lab

Combining genomics data and electronic health records, Biobank emerges as an important resource for precision health. With large-scale genetics/genomics data, genome-based disease risk prediction, polygenic risk score (PRS), is becoming an important tool for precision health. We are working on improving PRS, including the cross-ancestry adaptation, functional annotation incorporation, and better interpretation. Drug effect and side effect estimation are of great importance. By linking prescription records, genetics/genomics, and OMICs, biobanks provide an excellent opportunity to estimate drug effects and side effects at the molecular level in real-world data. We are working on causal-inference techniques and pharmaco-genomics for this purpose. We are grateful to the National Research Foundation of Korea (BP+ program) and Ministry of Food and Drug Safty for supporting our research. Our group develops statistical and computational methods for genetics/genomics data analysis and precision health, while leveraging multimodal data (images, videos, EHR data) for biomedical deep learning research and advancing drug discovery. Integrating Whole-Exome and Genotyping Array Data for Pharmacogenetic Typing: A Comparative Evaluation with Whole-Genome Sequencing in UK Biobank. Predicting Human-Gain Curves under Local Proxy Optimization from Repeated Ratings.