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.
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.
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, etc.).
In addition, we conduct polygenic risk scores and pharmacogenetic studies, and also contribute to analysis for microbiome & 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.
Three new preprints from the lab! One led by Shengxin introducing a novel framework to benchmark the individual-level clinical readiness of PRS for coronary artery disease (Liang, Kim et al., medRxiv), one led by Linke developing a ML approach for cross-platform proteomic imputation to address SomaScan–Olink discordance that has hindered replication of protein–disease associations (Li, Alaa et al., bioRxiv), and one co-led by Zhi reporting the largest CHIP GWAS and PheWAS to date (N>1 million) (Uddin, Yu et al., medRxiv).
Our paper co-led by Linke establishing link between CHIP and inflammatory bowel disease — and that the oral drug APX3330 can modify it — is now published in Blood (Kumar, Li et al.), with an accompanying podcast and review article.
Three papers accepted at Science Translational Medicine, Cell Genomics, and JAMA Cardiology!
Welcome Yushu to the group!
Congrats to Linke, Xinyu, Shengxin, Kathy, and Anthea on accepting their PhD offers! They are heading to Harvard University (x2: Statistical Genetics & Genetic Epidemiology, Cancer Epidemiology), University of Cambridge, and Duke University (x2: Computational Biology & Bioinformatics, Genetics & Genomics)!