AI Analysis of Super-resolution Microscopy: Biological Discovery in the Absence of Ground Truth

Overview

Journal J Cell Biol

Specialty Cell Biology

Date 2024 Jun 12

PMID 38865088

Authors

Ivan R Nabi

Ben Cardoen

Ismail M Khater

Guang Gao

Timothy H Wong

Ghassan Hamarneh

Affiliations

Soon will be listed here.

Abstract

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 the 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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