AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth
arxiv(2023)
摘要
Super-resolution microscopy, or nanoscopy, enables the use of
fluorescent-based molecular localization tools to study molecular structure at
the nanoscale level in the intact cell, bridging the mesoscale gap to classical
structural biology methodologies. Analysis of super-resolution data by
artificial intelligence (AI), such as machine learning, 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
paradigms to super-resolution microscopy and its potential to enable the
accelerated exploration of the nanoscale architecture of subcellular
macromolecules and organelles.
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