Statistical Bioinformatics Laboratory

We develop computational frameworks that uncover the dynamics of tumor evolution and tumor microenvironment (i.e., cell-type specific transcriptional activities), and clonal architecture across diverse cancer types. We have developed tools such as MuSE, to enable fast and accurate mutation calling, and DeMixT, to provide robust tumor-specific transcriptome deconvolution.

Additionally, with over 15 years of experience in cancer risk modeling, we utilize Bayesian statistics and machine learning to develop software tools for clinical cancer prevention and prognosis.

Our current research directions are:

1. Multi-omic deconvolution to study DNA–RNA dynamics in cancer, and
2. Cancer risk modeling using machine learning and Bayesian models.

In collaboration with clinicians and experimental biologists, we translate these insights into testable hypotheses and clinically meaningful advances.