We develop integrated annotations for the human genome using diverse data sources (e.g. sequence, ENCODE, Roadmap Epigenomics, GTEx) and apply these annotations to better analyze and interpret the association signals identified from numerous genome wide association studies. Our methods allow us to infer the most relevant tissue/cell types for a specific disease and the shared genetics across a set of diseases.

Leveraging annotation information and shared genetic basis across multiple diseases, we develop statistical methods to identify high risk individuals for better disease prevention.

We develop statistical methods to integrate different types of –omics data to infer perturbed pathways in cancer, identify drug targets from high-throughput screening data, and prioritize compound pairs that likely have synergistic effects. We also develop tools to identify biomarkers that are predictive patient treatment response.

In collaboration with Dr. Nenad Sestan at Yale, we develop statistical methods to probe the spatial-temporal expression patterns during human brain development, and use these inferred patterns to better identify genes for neurodegenerative and psychiatric disorders.

We have recently started to develop methods to capitalize on the longitudinal information in the rich medical records and –omics data to better predict patients disease progression and select more effective treatment.

We have long standing interest to infer biological networks from –omics data and integrate the inferred network information for disease mechanism and treatment studies. Our publications cover theoretical and methodological aspects of network reconstructions, and their applications to disease mechanism studies.

We develop statistical methods to address the unique challenges of single cell –omics data, including gene drop out, normalization, clustering, and joint analysis with bulk sequencing data.

We develop statistical methods to analyze microarray gene expression and genotyping data, next generation sequence data (including whole genome sequencing, whole exome sequencing, RNA-seq, ChIP-seq, and CLIP-seq), metagenomics data, proteomics data, and others.

In collaboration with Dr. Tommy Cheng at Yale, we develop systems biology tools to understand how herbal medicine may mediate the treatment effects of standard therapies.