UCI Public Health welcomed Min Zhang, MD, PhD, at the start of 2023 when she was appointed as a professor of epidemiology and biostatistics as well as the biostatistics shared resources director for the UCI Chao Family Comprehensive Cancer Center.
With two doctoral degrees in neuroscience and biological statistics & computational biology and another medical degree, Zhang has decades of extensive knowledge and expertise on both sides of diagnosis, treatment, and care of patients. As a biostatistician, Zhang specializes in statistical inference for “omics” data, which studies the whole genome/transcriptome/metabolome instead of specific molecules.
In the following Q&A interview, we get to know Zhang a little better to see how she plans to approach her work at UCI and the growing field of precision public health.
What does precision public health mean to you as a researcher?
Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Those same fundamentals can be harnessed into the public health field. More specifically, precision public health is about implementing the right intervention at the right time – every time to the right population.
My original training was as a physician and during my residency, I treated cancer patients. Almost 20 years ago, I saw firsthand how challenged physicians were in stopping the increased prevalence and mortality of cancer. That is when I realized I wanted to do something to predict the onset of the disease earlier and even prevent diseases from progressing into deadlier stages. An earlier diagnosis provides us with more treatment opportunities and I wanted to build the tools to react sooner before it was too late.
Tools to personalize what treatment plan we recommend has the potential to revolutionize our standard of care. As we learned through the pandemic, everyone reacted to the virus differently – some were hospitalized while others had minor symptoms. The diversity of our genetic makeup is an untapped world that I look forward to shedding light into.
What drew you to UCI Public Health and any opportunities for collaboration?
I have always known about how strong UCI’s cancer and neuroscience research was but the opportunity to strengthen precision public health and biostatistics is truly what brought me in. The statistical methodology my team and I have developed can be generalizable, meaning, we can address many types of cancers and even other neurodegenerative diseases. This opens up opportunities to collaborate across disciplines and bring interdisciplinary knowledge together. I’m interested in collaborating with people who have real data that are stuck with the current methods and need new statistical methodology to crack the problem.
What are your current research areas?
The culmination of my background has brought me to three research focus areas all under harnessing the power of biostatistics. First, the development of statistical methodology using Bayesian statistics, high dimensional data, multivariate statistics and variable selection. Second, the application of those methods to infer various “omics” data like genomic, epigenetic, transcriptomic, proteomic, and metabolomic. These types of data sets help us understand a person’s DNA, proteins, phenotypes, and more on a granular level. I utilize statistical methods for genome-wide association studies both at the family and population level. Third, the inferred relationship and mapping of certain markers is to ultimately help physicians inform their care plan and provide crucial data that can be used for tailored interventions.
Beyond my research portfolio, one of the most important aspects of my journey was exploring and learning science. If I could, I would stay in school learning forever. So instead, I’m excited to eventually bring my training grant funded by the National Cancer Institute to UCI to train the next generation on big data for cancer research. It is an intensive 10-day curriculum that gives students a real opportunity to work with big data. Many people may not realize that big data is BIG. It can’t be viewed on an excel sheet because the file can be as large as 1 terabyte. Each cell holds millions of data points and the data is only going to get bigger. This exposure is exactly what excited me to become a biostatistician.
The possibilities to discover are endless.
