Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from populations to a single cell. How to extract and understand non-trivial topological features and structures inherent in the networks is critical to understanding interactions within complicated biological systems. In this talk, I will introduce recent developments of machine learning algorithms that exploit spectral structures of networks for a wide range of biological applications, ranging from single-cell analysis to function prediction on protein-protein interaction networks.
This event is being run by the Department of Computer Science. Learn more here.