1. Develop AI-empowered systems biology model to study biological processes, relations, and functions by using multi-omics data. The classic systems biology model views biological processes as dynamic systems over biological networks. Three major types of biological networks include transcriptional regulatory, metabolic, and signaling networks, each having distinct biomechanical or molecular characteristics. We focus on developing novel representation forms of systems biological models to leverage identifiability, mathematical rationale, and biological interpretation in quantifying and studying biological processes by using omics data. The key challenge is that omics data usually only measures one-time points from each biological sample, hence additional assumptions need to be made by utilizing a large sample size or other information extraction approaches to approximate the dynamic characteristics of each biological system. We focus on single-cell or spatiallly resolved transcriptomics data as the biological process in individual cells is more purely determined by signals from the same cell.
2. Advancing the understanding of metabolic variations in human diseases and other health-related conditions. Metabolism affects the health- condition and living quality of humans. Almost all diseases have associated metabolic changes or risk factors. Dr. Zhang and Dr. Cao have long-term experiences in studying biochemical and metabolic changes in human diseases. Currently, the BDRL is focusing on the following topics:
3. Predict drug targets to improve the efficacy of immunotherapy. Numerous variations in the tumor microenvironment (TME) can cause a non-response effect of immunotherapy (PD-1 and other immune checkpoint inhibitors) in solid cancer. While substantial efforts have been made to quantify immuno-cell populations
4. Develop new AI frameworks or statistical approaches to solve mathematical problems in biological or general data sciences. Noting majority of questions in the study of biological data are transfer learning, which seeks to summarize biologically meaningful stories that could be utilized in further hypothesis raising and validation, our focuses include:
Ph.D. - The University of Georgia, Georgia, ATL 12/2015