Deep learning reduces data requirements and allows real-time measurements in imaging FCS-Reference-Cited by-同舟云学术

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Deep learning reduces data requirements and allows real-time measurements in imaging FCS

Published:2023-12 Issue: Volume: Page:
ISSN:0006-3495
Container-title:Biophysical Journal
language:en
Short-container-title:Biophysical Journal

Author:

Tang Wai Hoh^ORCID,Sim Shao Ren,Aik Daniel Ying Kia,Nelanuthala Ashwin Venkata Subba,Athilingam Thamarailingam,Röllin Adrian,Wohland Thorsten^ORCID

Funder

Ministry of Education - Singapore

Publisher

Elsevier BV

Subject

Biophysics

Reference54 articles.

1. MINSTED nanoscopy enters the Ångström localization range;Weber;Nat. Biotechnol.,2023

2. Ångström-resolution fluorescence microscopy;Reinhardt;Nature,2023

3. The performance of 2D array detectors for light sheet based fluorescence correlation spectroscopy;Singh;Opt Express,2013

4. Multiplexed measurement of protein diffusion in Caenorhabditis elegans embryos with SPIM-FCS;Struntz;J. Phys. D Appl. Phys.,2015

5. ImFCS: a software for imaging FCS data analysis and visualization;Sankaran;Opt Express,2010

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantification of membrane fluidity in bacteria using TIR-FCS;Biophysical Journal;2024-08

2. Machine learning and advanced statistical analysis for fluorescence correlation spectroscopy;Biophysical Journal;2024-03

3. Evaluation of functional transbilayer coupling in live cells by controlled lipid exchange and imaging fluorescence correlation spectroscopy;Methods in Enzymology;2024

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