ArticleNature communications2022
Bayesian deep learning for error estimation in the analysis of anomalous diffusion.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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18 citing papers in PubMed.
- Diffusional aging at water/oil interfaces laden with charged nanoparticles studied by single-molecule tracking.Nature communications · 2026Article
- Characterization of Parameter Uncertainty in Global Analysis for Ultrafast Spectroscopy Using Markov Chain Monte Carlo Sampling.Precision chemistry · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- Single-trajectory Bayesian modeling reveals multi-state diffusion of the MSH sliding clamp.NPJ systems biology and applications · 2026Article
- 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
- Pharmacokinetic modelling during long-term anesthesia: minimizing the gap.Journal of advanced research · 2026Article
- Exploring how deep learning decodes anomalous diffusion via Grad-CAM.Nature communications · 2026Article
- Bayesian deep learning for probabilistic aquifer vulnerability and uncertainty prediction.Scientific reports · 2026Article
- Quantitative evaluation of methods to analyze motion changes in single-particle experiments.Nature communications · 2025Article
- Physics-informed deep learning for stochastic particle dynamics estimation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Robust Quantification of Live-Cell Single-Molecule Tracking Data for Fluorophores with Different Photophysical Properties.The journal of physical chemistry. B · 2024Article
- Diffusion in Porous Rock Is Anomalous.Environmental science & technology · 2024Article
- Inferring pointwise diffusion properties of single trajectories with deep learning.Biophysical journal · 2023Article
- Compartmentalization of androgen receptors at endogenous genes in living cells.Nucleic acids research · 2023Article
- Heterogeneous biological membranes regulate protein partitioning via fluctuating diffusivity.PNAS nexus · 2023Article
- Ensemble heterogeneity mimics ageing for endosomal dynamics within eukaryotic cells.Scientific reports · 2023Article
- Plasmodium sporozoite search strategy to locate hotspots of blood vessel invasion.Nature communications · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusion model and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a well-calibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.
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