Our goal is to connect our expertise in bioinformatics, computer science, and statistical genetics to better diagnosis and treatment of diseases.
As a part of the Department of Medicine, we have two main roles:
(1) We develop advanced methods and algorithms for genetic and genomic analysis, to enhance our understanding of the human genome.
(2) We extensively collaborate with clinical researchers to help design, analysis, and interpretation of the genomic analysis for clinical/translational research.
Roles (1) and (2) are interconnected; working closely with clinical researchers provides us a unique opportunity to identify unmet needs of new methods, and helps us to develop practical solutions that can be directly applied to clinics.
We are applying various machine learning and deep learning algorithm to many biological problems and medical big data.
Statistical methods for genetic association studies
We make novel computational and statistical approaches to facilitate design, quality control, and analysis of genome-wide association studies.
HLA imputation &
We make imputation approach for HLA region and fine-map MHC region which is a highly challenging region for genetic studies.
We develop new methods for meta-analysis that increases power. We expand the application of meta-analysis to eQTL analysis and GxE interaction detection.
We develop new methods to address clinical heterogeneity in a cohort caused by sample misclassifications or other reasons. We aim to detect, measure, and correct for heterogeneity.
We are aiming at discovering novel causal relationships and verify assessing associations with clinical end points. Again, we develop several extensions to MR approaches.
Next Generation Sequencing (NGS) &
We analyze the cancer omics data and establish a methodology to predict the survival outcome after surgery.
Also, we are interested in innovative pathway & network analysis.
Drug discovery &
We identify new targets of diseases based on genomic data, and develop new algorithm to implement personalized medicine.