AI Article Synopsis
- Super-resolution microscopy, or nanoscopy, allows researchers to investigate molecular structures at the nanoscale within living cells, connecting modern imaging techniques to traditional structural biology.
- AI and machine learning can analyze super-resolution data, opening new avenues for biological discoveries that were previously unknown or lacked established knowledge.
- The use of weakly supervised learning methods in super-resolution microscopy can enhance the understanding of the nanoscale architecture of macromolecules and organelles, speeding up research and discoveries in this field.
Article 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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11169916 | PMC |
| http://dx.doi.org/10.1083/jcb.202311073 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!