Zuoheng (Anita) Wang, PhD
Professor of Biostatistics, Biomedical Informatics & Data Science
Dr. Zuoheng Wang is a Professor of Biostatistics, Computational Biology and Bioinformatics, and Biomedical Informatics & Data Science at Yale University. Her research focuses on statistical modeling of omics and healthcare data. Dr. Wang’s statistical expertise spans kernel machine methods, mixed effects models, correlated and longitudinal data analysis, machine learning, and network analysis. She develops innovative statistical methods and computational tools for large-scale biomedical studies and electronic health records data, including disease risk prediction, genetic susceptibility variant identification, and single-cell and spatial transcriptomic data analysis, with application in mental health, addiction, cancer, autism, lung and cardiovascular diseases, contributing to the understanding of disease pathogenesis. Her research has been continuously supported by grants from the NIH and NSF. She has published over 100 peer-reviewed papers, including first or senior author papers in leading journals such as Genome Biology, Nucleic Acids Research, The American Journal of Human Genetics, PLoS Computational Biology, Journal of the American Medical Informatics Association, and Journal of the American Statistical Association. She currently serves as an Associate Editor for Statistics Innovation and BMC Genomics.
What do you do with Data Science?
I have a strong record of developing innovative statistical methods and computational tools for large-scale biomedical studies, with applications in disease risk prediction, genetic susceptibility variant identification, and single-cell and spatial transcriptomics data analysis. My research spans diverse disease areas, including mental health, addiction, cancer, autism, and cardiopulmonary diseases. Previous work in data science includes integrating electronic health record (EHR) and genetic data, incorporating both health and family history, for disease risk prediction; leveraging external drug knowledge for comparative effectiveness analyses in real-world prescription data; and developing deep learning approaches to model cell-cell interactions in spatial transcriptomics. I am committed to advancing methodologies for the analysis of patient data, including both clinical and high-throughput omics data, to improve understanding of disease heterogeneity and inform precision medicine.