About
Dr. Zhana Duren is an assistant professor at Clemson University. He earned his BS in Mathematics and Applied Mathematics from Beihang University (China) in 2012 and his Ph.D in Operational Research and Cybernetics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2017. During his doctoral studies, Dr. Duren focused on developing computational methods for analyzing high-throughput genomic data. From 2015 to 2020, Dr. Duren worked in Professor Wing Hung Wong’s lab at Stanford University as a visiting Ph.D student (2015-2017) and postdoctoral research fellow (2017-2020). His research during this time focused on developing statistical and computational methods for gene regulatory network, single cell genomics, and interpretation of disease associated genetic variants. In 2020, Dr. Duren joined Clemson University as an assistant professor. His current research interests include developing statistical and computational methods for analyzing genomic data and using these methods to gain insight into the precision medicine. Dr. Duren is committed to advancing the field of genomics research and training the next generation of computational biologists.
Visit Dr. Duren's Faculty Profile.
How their research is transforming health care
The efficacy of some commonly prescribed drugs is limited to less than 25% of patients due to the influence of personal genetic variants on gene regulatory networks. This phenomenon underscores the importance of personalized medicine, which focuses on understanding the impact of genetic variants on an individual's response to treatment. A person’s genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. It is challenging to understand the mechanism of how these genetic variants contribute to disease because over 90% of trait-associated genetic variants are located in non-coding regions which don’t encode any protein-coding genes but may have regulatory functions. The development of personalized gene regulatory networks is therefore essential for tailoring treatments to individual patients and improving therapeutic outcomes. The long-term goal of Duren lab is to explain mechanistically how non-coding genetic variants affect cellular and environmental context-dependent gene regulatory networks and influence phenotypes. To do this, we develop novel statistical machine learning methods and bioinformatics tools. Specific directions include 1) inference method of cellular and environmental context-dependent gene regulatory network by integrating different types of genomics data, 2) single-cell genomics data analysis, and 3) identification of causal variants for complex diseases by using cellular and environmental context specific gene regulatory networks.
Health research keywords
Faculty Scholar, Precision medicine and personalized gene regulatory network, Drug addiction, Disease associated to aging, Natural selection and adaptation, Single cell genomics, Data integration, Computational system biology.
