Systems Biology of Disease

Catalog description

Applies mathematical, statistical and machine learning methods to real-world patient datasets to make biologically or clinically meaningful decisions. Specifically, students learn to cluster single-cell data to define cell types, develop a classifier of disease status, learn how to assess the statistical significance of overlaps between genes or other biological entities, use multi-omic data to construct meaningful networks, and finally, how to project dynamics onto scRNA-seq networks using RNA velocity. Uses primary literature to discuss exemplary applications of these systems biology methodologies for medical applications. A final project requires students to apply at least one of the tools and/or concepts discussed in class on a real-world dataset of their choosing.