- Statistical and computational modelling of cancer initiation and progression, including energy metabolism reprogramming, interactions between cancer cells and microenvironment
- Intra-tumor heterogeneity and its relationship with cancer associated micro-environmental stress
- Prediction of gain or loss of function of certain mutations and interactive effect of multiple mutations
- Develop statistical deconvolution method to predict the alterations of immune and stroma cells in tissue based omics data.
- Develop novel computation methods to integrate multiple omics types to model the biological characteristics in progression of inflammatory diseases and cancer
- Single cell RNA-Seq data analysis
- Bi-clustering algorithm development
Dr. Chi Zhang focuses on computational modeling of cancer micro-environment including the level of hypoxia, oxidative stress, acidity and dysregulation of extracellular matrix as well as altered immune responses in cancer tissue by using large scale omics data. Dr. Chi Zhang is also interested in developing novel computation methods to integrate multiple tissue level and single cell omics data types to understand the mechanism of cancer initiation, progression, metastasis and cancer tumor tissue’s resistance to certain therapies. In addition, his research application also includes inference of intra-tumor heterogeneity and reprogrammed metabolism, and prediction of gain or loss of functions led by a certain mutation or collective effect of multiple mutations.
Currently, I mainly focused on (1) independent research in computational modeling of disease microenvironment, cell type specific activity and transcriptional regulation, and computational algorithm development, and (2) collaborative research in identification of biological characteristics related to disease progression and drug resistance.
- Computational modeling of cancer microenvironment
- Role of cancer microenvironment in cancer progression, and drug resistance.
- Cell-cell interaction in a disease microenvironment
- Utilizing single cell RNA-seq/FISH data to improve the understanding of a complex tissue
- Bi-clustering and local low rank pattern identification in high dimensional data
- Knowledge graph and tensor decomposition
- Computational model of drug resistance mechanisms