The explosion of large-scale, high-throughput technologies in the biological sciences demands a comprehensive view of biological systems.   Dr. Zhu’s research focuses on:

  1. Method development in integrating diverse high-throughput data into probabilistic causal network models. There are multiple risk factors as well as their interactions contributing to complex human diseases, such genetic background, infection, environmental states, life-style choices, and etc.   It is critical to integrate all these factors into comprehensive disease networks which provide a context to understand mechanism of diseases and to study effects of genetic perturbations, drug perturbations, and their interactions.  Dr. Zhu’s group has developed many methods for integrating genetic and genomic data into Bayesian networks:
  1. Method development in comparing multiple networks. There are multiple risk/perturbation factors driving a biological system from a healthy state to a disease state.  In response, there are hundreds to thousands genes changed at transcription or protein levels.  Dr. Zhu’s works showed that instead of monitoring changes in a few genes, it is more robust to monitor changes of network states or structures when studying complex diseases:
  1. Method development in analyzing biological networks. If comprehensive biological networks are available, how to convert network knowledge into actionable items?  For example in drug discovery and development, we investigated which nodes and node combinations in the networks can generate largest beneficial effects when perturbed.  In biomarker research, we examined which parts of networks can robustly connect to drug response, disease progression and survivals.