Computational Multi-omics for Age-related Diseases

Our research focuses on computational modeling of human multi-omics data to uncover the mechanisms driving cardiovascular and other age-related diseases, aiming to identify personalized strategies for disease prevention and treatment.

Key areas of interest:

• Aging-clonal hematopoiesis-associated diseases axis: We are passionate about understanding the biology connecting aging, clonal hematopoiesis (clonal expansions in the blood-forming system), and associated diseases. We discover interventions capable of breaking or modifying the links across the three pillars.

• Omics methods development: While we apply multi-omics data to biological questions, we actively tackle fundamental methodological challenges inherent to the data itself (e.g., discordance between proteomics platforms, omics instruments, and interpretability of large-scale proteomics).

• Multi-modal medical information & machine learning: We use deep learning for imaging (human and mouse) and LLMs to process patient information from EHR. For both areas, we further integrate genomics data to discover biological insights and advance our understanding of aging-clonal hematopoiesis-associated diseases.

• In addition, we conduct polygenic risk scores and pharmacogenetic studies, and also contribute to analysis for hematological cancer clinical trials, the Somatic Mutation Across Human Tissues (SMAHT) consortium, and Bermuda Genomics.

Our research is largely strengthened by the extensive human and murine datasets we have access to, and we are deeply grateful to our collaborators and cohort PIs for their trust.