Our research goal is to interpret and distill the complexity of cancer and other rare diseases through integration of large scale multi-omics data using dynamic modeling, graph theory, and machine learning methods. We aim to apply these methods to study challenging cancer biology problems, particularly how chromatin alterations influence cellular phenotypes in response to genetics, environments, and pharmacological perturbations. By integrating large datasets, we hope to extract relevant information necessary to make precise biological and clinical predictions and computationally direct experiments. The primary focus of our lab is to produce high-resolution computational models to study the effects of genetic and epigenetic perturbations on chromatin alterations that affect cellular states, elucidating the molecular mechanisms of cancer and other diseases.
We are looking for CSB/HSG/MGG/CS/MATH/EE/BME graduate students to rotate in our lab!
- Cancer systems biology
— Chromatin remodeling in cancer (histone modifications)
— Cancer subtyping
— Tumor microenvironment
— Targeting cancer stemness pathway in breast cancer
— Single-cell approaches to address tumor heterogeneity
— Pharmacodynamics & pharmacokinetics of anti-cancer drugs
— Biomarker discovery in cancer
— Cancer immunotherapy
- Modeling gene regulatory networks
- Genotype-phenotype correlation
- Natural language processing