AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth

Abstract

The nanoscale resolution of super-resolution microscopy has now enabled the use of fluorescent based molecular localization tools to study whole cell structural biology. Machine learning based analysis of super-resolution data offers tremendous potential for discovery of new biology, that by definition is not known and lacks ground truth. Herein, we describe the application of weakly supervised learning paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the molecular architecture of subcellular macromolecules and organelles.