top of page

New methods for analyzing biological networks

We create new algorithms that are specifically designed to identify disease-driving network structures. ALPACA compares regulatory network structure between cases and controls to find disease modules that are specific to disease. CRANE estimates the statistical significance of changes in network modular structure. TIGER simultaneously estimates transcription factor activities and regulatory network structure using Bayesian matrix factorization Our methods are open-source and available on our lab Github page and through the NetZoo package.

Regulatory circuits driving Merkel cell carcinoma

Health disparities in colorectal cancer

Colorectal cancer differentially impacts certain demographics, including younger individuals and African Americans. We are identifying changes in the genome, transcriptome, and methylome that are associated with early-onset and African American colorectal cancer. 

Merkel cell carcinoma is a neuroendocrine cancer that can be caused by a virus. Using this virus, we can generate "cancer in a test tube" and observe how neuroendocrine tumors develop. Using genomic profiling, we create dynamic regulatory network models to understand how normal skin cells are reprogrammed into a precancerous neuroendocrine state - and identify drugs that can reverse this process.

bottom of page