This project addresses the need for statistical models that help biomedical researchers better understand changes in biological markers across populations and within individual subjects over time, using multidimensional tabular data. The investigators will collaborate with neuroscientists at the University of Iowa College of Medicine to adapt and enhance statistical modeling approaches for analyzing data from real-world biomedical studies involving mice. This work seeks to address some of the fundamental statistical challenges associated with these complex datasets. The investigators will accomplish this by leveraging two types of time-dependent modeling approaches and extending these methods to datasets that contain far more variables than observations. The project is significant because these methods will assist biomedical researchers in tackling key healthcare questions, accelerating scientific discovery, and providing tools for interpreting complex biomedical data. To promote broad accessibility and impact, the methods and tools developed will be released as open-source software, advancing both statistical methodology and biomedical research. The project will also strengthen data science training for graduate students and contribute to curriculum development at the undergraduate and graduate levels, thereby preparing students for careers in academia and the biomedical industry. The investigators are committed to mentoring graduate students in both methodological innovatio