ArticleBiophysical journal2024
Deep learning reduces data requirements and allows real-time measurements in imaging FCS.
Article in Biophysical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Mapping Protein Diffusion from the Plasma Membrane to the Nucleus: Insights from Fluorescence Correlation Spectroscopy.Biomolecules · 2026Review
- Measuring morphogen transport over multiple spatial scales in live zebrafish embryos.Nature communications · 2026Article
- Article
- Unlocking the black box: multimodal imaging and quantitative analysis of plant vesicular trafficking.Advanced biotechnology · 2026Review
- Boxcar Imaging FCS Reveals Membrane Raft Stabilization Kinetics in Antigen-Stimulated Mast Cells.bioRxiv : the preprint server for biology · 2025Article
- Super-resolution algorithms for imaging FCS enhancement: A comparative study.Biophysical journal · 2025Article
- Fluorescence correlation spectroscopy for particle sizing: A notorious challenge.Biophysical journal · 2025Review
- FRET-FCS: Advancing comprehensive insights into complex biological systems.Biophysical journal · 2025Review
- Biomolecular Condensates as Emerging Biomaterials: Functional Mechanisms and Advances in Computational and Experimental Approaches.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- From Neural Networks to Transformers: Achieving High and Fast Precision in Fluorescence Correlation Spectroscopy Analysis.ACS omega · 2025Article
- Article
- Quantification of membrane fluidity in bacteria using TIR-FCS.Biophysical journal · 2024Article
- Machine learning and advanced statistical analysis for fluorescence correlation spectroscopy.Biophysical journal · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Imaging fluorescence correlation spectroscopy (FCS) is a powerful tool to extract information on molecular mobilities, actions, and interactions in live cells, tissues, and organisms. Nevertheless, several limitations restrict its applicability. First, FCS is data hungry, requiring 50,000 frames at 1-ms time resolution to obtain accurate parameter estimates. Second, the data size makes evaluation slow. Third, as FCS evaluation is model dependent, data evaluation is significantly slowed unless analytic models are available. Here, we introduce two convolutional neural networks-FCSNet and ImFCSNet-for correlation and intensity trace analysis, respectively. FCSNet robustly predicts parameters in 2D and 3D live samples. ImFCSNet reduces the amount of data required for accurate parameter retrieval by at least one order of magnitude and makes correct estimates even in moderately defocused samples. Both convolutional neural networks are trained on simulated data, are model agnostic, and allow autonomous, real-time evaluation of imaging FCS measurements.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.