Session

DescriptionIn recent years there has been substantial interest in using machine learning and AI algorithms for data-driven scientific discovery. This interest has to a large degree been fueled by significant increases in the power of high-performance computing coupled with growing availability of massive data sets, ranging from weather and climate simulations to biological studies of the host response to infectious disease. This computational and data driven research has led to a number of significant discoveries related to, e.g., protein-protein interactions, biomarkers of infectious disease, molecular neuroscience, immunology, cancer, and structural biology. This minisymposium will highlight recent work being done at the interface of high-performance computing, algorithms, mathematics, and computing for understanding complex systems biological systems. It will feature simulations and AI analyses using high-performance computing resources at Oak Ridge National Labs and the National Center for Atmospheric Research (NCAR). The focus will be on health-related applications, including modeling pathogen emergence in relation to climate change, graph learning and statistical shape analysis for understanding complex biological systems and learning neural activity patterns from deep geometric and topological networks.