Deep learning in single-molecule imaging and analysis: recent advances and prospects.
Journal
Chemical science
ISSN: 2041-6520
Titre abrégé: Chem Sci
Pays: England
ID NLM: 101545951
Informations de publication
Date de publication:
26 Oct 2022
26 Oct 2022
Historique:
received:
02
05
2022
accepted:
19
09
2022
entrez:
9
11
2022
pubmed:
10
11
2022
medline:
10
11
2022
Statut:
epublish
Résumé
Single-molecule microscopy is advantageous in characterizing heterogeneous dynamics at the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest from single-molecule data. As a new class of computer algorithms that simulate the human brain to extract data features, deep learning networks excel in task parallelism and model generalization, and are well-suited for handling nonlinear functions and extracting weak features, which provide a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advances in the application of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.
Identifiants
pubmed: 36349113
doi: 10.1039/d2sc02443h
pii: d2sc02443h
pmc: PMC9600384
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
11964-11980Informations de copyright
This journal is © The Royal Society of Chemistry.
Déclaration de conflit d'intérêts
There are no conflicts to declare.
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