ArticleScientific reports2019
Particle Mobility Analysis Using Deep Learning and the Moment Scaling Spectrum.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Characterizing cellular receptor dynamics in live fluorescence imaging using stochastic modeling and machine learning.iScience · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data.Cells · 2026Review
- Exploring how deep learning decodes anomalous diffusion via Grad-CAM.Nature communications · 2026Article
- Feature Selection and Hyperparameter Optimization for Machine Learned Classification of 3D Single-Particle Tracking.Chemical & biomedical imaging · 2026Article
- Leveraging Artificial Intelligence for Advancements in Liquid Dosage Formulations in the Pharmaceutical Industry.Therapeutic innovation & regulatory science · 2025Review
- Pointwise prediction of protein diffusive properties using machine learning.JPhys photonics · 2025Article
- Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function.Nature methods · 2025Article
- Physics-informed deep learning for stochastic particle dynamics estimation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Distinct mobility patterns of BRCA2 molecules at DNA damage sites.Nucleic acids research · 2024Article
- Deep learning reduces data requirements and allows real-time measurements in imaging FCS.Biophysical journal · 2024Article
- Inferring pointwise diffusion properties of single trajectories with deep learning.Biophysical journal · 2023Article
- Extract latent features of single-particle trajectories with historical experience learning.Biophysical journal · 2023Article
- Compartmentalization of androgen receptors at endogenous genes in living cells.Nucleic acids research · 2023Article
- Quantitative Single-Molecule Localization Microscopy.Annual review of biophysics · 2023Review
- TrackAnalyzer: A Fiji/ImageJ toolbox for a holistic analysis of tracks.Biological imaging · 2023Article
- Single-molecule analysis of intracellular insulin granule behavior and its application to analyzing cytoskeletal dependence and pathophysiological implications.Frontiers in physiology · 2023Article
- A generative model to simulate spatiotemporal dynamics of biomolecules in cells.Biological imaging · 2023Article
- MPTHub: An Open-Source Software for Characterizing the Transport of Particles in Biorelevant Media.Nanomaterials (Basel, Switzerland) · 2022Article
- Objective comparison of methods to decode anomalous diffusion.Nature communications · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative analysis of dynamic processes in living cells using time-lapse microscopy requires not only accurate tracking of every particle in the images, but also reliable extraction of biologically relevant parameters from the resulting trajectories. Whereas many methods exist to perform the tracking task, there is still a lack of robust solutions for subsequent parameter extraction and analysis. Here a novel method is presented to address this need. It uses for the first time a deep learning approach to segment single particle trajectories into consistent tracklets (trajectory segments that exhibit one type of motion) and then performs moment scaling spectrum analysis of the tracklets to estimate the number of mobility classes and their associated parameters, providing rich fundamental knowledge about the behavior of the particles under study. Experiments on in-house datasets as well as publicly available particle tracking data for a wide range of proteins with different dynamic behavior demonstrate the broad applicability of the method.
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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.