Assistant Professor of Statistics Weining Shen’s recent paper in Circulation, a top journal in the field of cardiology, focuses on a complex topic — cardiac hypertrophic growth — but the main goal of his work is simple: apply statistics to improve healthcare. The paper, “Lin28a Regulates Pathological Cardiac Hypertrophic Growth through Pck2-Mediated Enhancement of Anabolic Synthesis,” stems from Shen’s work with Dr. Li Qian and Jiandong Liu at the University of North Carolina at Chapel Hill, and it isn’t the first paper resulting from Shen’s ongoing collaboration with Qian, Liu and their lab group.
“Heart disease is the leading cause of death in the United States and around the world,” notes Shen. “My collaborators and I are interested in studying the biological process behind the heart cells and genes with a long-term goal of helping to develop new therapeutic options for the treatment of heart diseases.” Shen is thus using statistics to model this biological process. “With the help of statistical modeling for next-generation sequencing data, we showed that Lin28a plays a key role in facilitating the cardiac metabolism switch and the heart enlargement, which may provide potential treatments for patients who suffer from heart failure and other related diseases.”
An earlier paper that came out of this work, “Single-Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte,” appeared in Nature. Focused on direct cardiac reprogramming, the paper explains how Shen used data from Qian’s experiments with mice to model the cardiac reprogramming based on single cell RNA-sequencing analysis, which helps create a better understanding for healing the scar tissue from heart diseases.
This is just one of Shen’s several collaborations in the field of healthcare. He has also worked with statisticians and physicians at the University of Texas MD Anderson Cancer Center and the University of Michigan. That work, published in Biometrics, focused on developing a new scoring system to help predict the risk of developing liver cancer using information from biomarkers and other patient information.
“We came up with some new statistical models to score every patient and predict their likelihood of developing liver cancer,” says Shen. Using blood work, they focused on detecting out-of-range indexes to reveal latent patterns that can indicate cancer risks. “The role of the statistician is to extract and summarize the useful information out of hundreds or even hundreds of thousands of indexes to boost the prediction accuracy of certain diseases.”
All of this work feeds into a five-year grant Shen received from the Simons Foundation in 2017 to promote collaboration between mathematicians and statisticians with other domain experts. The project, “Statistical Modeling of Complex Data: Theory and Methods,” supports Shen’s multidisciplinary collaborations with the goal of developing flexible, efficient and robust statistical methods for modeling complex data. “A good model can be generalized to address other problems — that’s the beauty of statistics. It lets you play in everyone’s backyard,” says Shen. “Living in the era of big data, I’m glad to see more and more people starting to appreciate the significant role of statistics.”
— Shani Murray