Machine learning for cross-scale microscopy of viruses.
Cell Rep Methods
; 3(9): 100557, 2023 09 25.
Article
in En
| MEDLINE
| ID: mdl-37751685
ABSTRACT
Despite advances in virological sciences and antiviral research, viruses continue to emerge, circulate, and threaten public health. We still lack a comprehensive understanding of how cells and individuals remain susceptible to infectious agents. This deficiency is in part due to the complexity of viruses, including the cell states controlling virus-host interactions. Microscopy samples distinct cellular infection stages in a multi-parametric, time-resolved manner at molecular resolution and is increasingly enhanced by machine learning and deep learning. Here we discuss how state-of-the-art artificial intelligence (AI) augments light and electron microscopy and advances virological research of cells. We describe current procedures for image denoising, object segmentation, tracking, classification, and super-resolution and showcase examples of how AI has improved the acquisition and analyses of microscopy data. The power of AI-enhanced microscopy will continue to help unravel virus infection mechanisms, develop antiviral agents, and improve viral vectors.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Viruses
/
Microscopy
Limits:
Humans
Language:
En
Journal:
Cell Rep Methods
Year:
2023
Document type:
Article
Publication country:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA