ArticleNature communications2021
Objective comparison of methods to decode anomalous diffusion.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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
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
63 citing papers in PubMed, 9 citations in OpenAlex.
- Characterizing cellular receptor dynamics in live fluorescence imaging using stochastic modeling and machine learning.iScience · 2026Article
- Transient Charging of Mixed Ionic-Electronic Conductors by Anomalous Diffusion.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- ExoFILT: transfer learning for robust and accelerated analysis of exocytosis single-particle tracking data.Bioinformatics (Oxford, England) · 2026Article
- Diffusional aging at water/oil interfaces laden with charged nanoparticles studied by single-molecule tracking.Nature communications · 2026Article
- Solution-Tunable Interfacial Interaction Landscape Governs Anomalous Nanoparticle Diffusion in Liquid-Phase Electron Microscopy.ACS nano · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- DeepTRACE brings flexible machine learning to single-molecule track analysis.Communications biology · 2026Article
- Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data.Cells · 2026Review
- Detecting directed motion and confinement in single-particle trajectories using hidden variables.eLife · 2026Article
- Stochastic intracellular calcium dynamics show preserved structures identified by deep learning classification.PLoS computational biology · 2026Article
- Exploring how deep learning decodes anomalous diffusion via Grad-CAM.Nature communications · 2026Article
- Deep Learning Tools for Single Quantum Dot Tracking.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Revealing plasma membrane protein dynamics in living plant cells with single-molecule tracking.Journal of experimental botany · 2025Article
- Cellular Distribution and Motion of Essential Magnetosome Proteins Expressed in Mammalian Cells.Biosensors · 2025Article
- Anomalous Dynamics of Superparamagnetic Colloidal Microrobots with Tailored Statistics.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Perspective: An outlook on fluorescence tracking.The Journal of chemical physics · 2025Article
- Scaling laws of political regime dynamics: stability of democracies and autocracies in the twentieth century.Royal Society open science · 2025Article
- Pointwise prediction of protein diffusive properties using machine learning.JPhys photonics · 2025Article
- Quantitative evaluation of methods to analyze motion changes in single-particle experiments.Nature communications · 2025Article
- Learning the diffusion of nanoparticles in liquid phase TEM via physics-informed generative AI.Nature communications · 2025Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
34 authors at 18 institutions in 12 countries.
Funding
Abstract
Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.
Identifiers
What OpenQuestion holds
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.