ReviewChemical science2022
Deep learning in single-molecule imaging and analysis: recent advances and prospects.
Review in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
The trial behind it
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Who cites it
12 citing papers in PubMed.
- Fused deep learning enables 6D single-molecule localization in polarization-resolved microscopy.Methods and applications in fluorescence · 2026Article
- Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.Nature communications · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- A General Analytic Approach for Rapid Diagnostics by a Simple Algorithm for Fluorescence Single Molecule Counting.Biosensors · 2026Article
- Single-Molecule Techniques for Probing the Conformation, Dynamics and Interactions of Intrinsically Disordered Proteins.Biophysics reviews · 2025Article
- Discriminating single-molecule binding events from diffraction-limited fluorescence.Nature communications · 2025Article
- Nanobiosensors for Single-Molecule Diagnostics: Toward Integration with Super-Resolution Imaging.Biosensors · 2025Review
- Super-Resolution Mapping and Quantification of Molecular Diffusion via Single-Molecule Displacement/Diffusivity Mapping (SMAccounts of chemical research · 2025Article
- Single-molecule imaging for investigating the transcriptional control.Molecules and cells · 2025Review
- Deep learning based local feature classification to automatically identify single molecule fluorescence events.Communications biology · 2024Article
- Trends in Single-Molecule Total Internal Reflection Fluorescence Imaging and Their Biological Applications with Lab-on-a-Chip Technology.Sensors (Basel, Switzerland) · 2023Review
- From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.International journal of molecular sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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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.