Our Research


The goal of our research is to discover new insights that improve our understanding of complicated biological systems. As a group, the MezeyLab is working on a variety of computational method development and data analysis research projects, where each is focused on answering specific questions about a biological system, a data application, or an analysis methodology. Our research projects are initiated by working closely with domain experts and collaborators to determine what a data type can reliably measure about a biological system so we can understand both the potential and limits of what can be extracted from these data and apply the computational analysis methodology that can return the most precise insights or predictions attainable.

The output from our research projects includes theory, computational statistics methods, machine learning methods, algorithms, and analysis frameworks developed for analyzing genomic (and up), image, clinical (trial to real world), and electronic health record data with applications in four major areas: studying the structure and impacts of Genome Variation in populations, Statistical Genetics analysis of rare and complex diseases, Network Discovery and causal modeling, and improving Disease Risk Prediction and treatment. Please see our publications and the following pages for representative examples of our research on: