SuperResNET - Learning In-Cell Macromolecular Architecture from SMLM Data

Attend this 52-minute keynote webinar to explore how artificial intelligence revolutionizes super-resolution microscopy analysis for understanding cellular molecular architecture. Learn about SuperResNET, an integrated machine learning-based software that visualizes and quantifies 3D point cloud data from single molecule localization microscopy (SMLM), presented by Dr. Ivan Robert Nabi from the University of British Columbia. Discover how this computational tool bridges the gap between atomic-level protein structures and their organization within intact cells by providing modules for fluorophore blinking correction, denoising, segmentation, clustering, and feature extraction. Examine real-world applications including molecular analysis of nucleopore complexes, structural changes in clathrin-coated pits, and characterization of caveolae and caveolin-1 scaffolds. Understand how SMLM technology breaks the diffraction barrier and compare SuperResNET's structure determination capabilities to cryoEM methods. Explore recent software updates that enable two-channel interaction distance analysis for studying protein interactions within macromolecular assemblies, and gain insights into network graph analysis for determining molecular structure from dSTORM and MinFlux microscopy data. Participate in live Q&A sessions and earn PACE continuing education credits upon completion.