Dr. Chen is a distinguished MPower Professor of Biostatistics and Bioinformatics. His research focuses on modeling complex structured biomedical data, including spatiotemporal dependence in neuroimaging, linkage disequilibrium in genetics, co-expression graph structures in epigenetics, transcriptomics, proteomics, metabolomics, and trustworthy AI/ML in real-world evidence (RWE) data inference. He is also involved in developing machine learning models for individual-level inference, considering complex dependencies between high-throughput features. The overarching goal of Dr. Chen's methodology research is to improve the accuracy, reliability, and cross-study replicability through advanced statistical and ML/AI models. Dr. Chen has broad experience in collaborative biostatistical research, including clinical trials, environmental health, infectious disease, and cancer research.

Co-expression network multivariate regression                                                                                                                                                                                                                                                                             Abstract: Accounting for dependence among high-dimensional variables in omics data analysis is critical to obtain accurate and reliable statistical inference. Although latent, omics variables often exhibit structured correlation/co-expression patterns. However, there are few methods explicitly accounting for such structured dependence in the statistical analysis of omics data (e.g. differential expression analysis). To address this methodological gap, we propose a Coexpression network multivariate Regression (CoReg), which integrates co-expression network structure into multivariate regression analysis to precisely account for the inter-correlations (dependence) among omics variables. We show in simulations that CoReg substantially improves the accuracy of statistical inference and replicability across studies. These findings suggest that CoReg provides an alternative approach for omics data association analysis with dependence adjustment, analogous to the role of mixed-effects models in handling repeated measures in lower-dimensional settings.