ArticleScientific reports2015
Estimating the anomalous diffusion exponent for single particle tracking data with measurement errors - An alternative approach.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Nanoscale domains govern local diffusion and ageing within fused-in-sarcoma condensates.Nature nanotechnology · 2026Article
- Non-Markovian Diffusion and Adsorption-Desorption Dynamics: Analytical and Numerical Results.Entropy (Basel, Switzerland) · 2024Article
- TrajPy: empowering feature engineering for trajectory analysis across domains.Bioinformatics advances · 2024Article
- Objective comparison of methods to decode anomalous diffusion.Nature communications · 2021Article
- Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Detection of Anomalous Diffusion with Deep Residual Networks.Entropy (Basel, Switzerland) · 2021Article
- Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion.Entropy (Basel, Switzerland) · 2020Article
- Single-Particle Diffusion Characterization by Deep Learning.Biophysical journal · 2019Article
- A Jump-Distance-Based Parameter Inference Scheme for Particulate Trajectories.Biophysical journal · 2019Article
- Identifying diffusive motions in single-particle trajectories on the plasma membrane via fractional time-series models.Physical review. E · 2019Article
- Fluid heterogeneity detection based on the asymptotic distribution of the time-averaged mean squared displacement in single particle tracking experiments.Journal of physics. A, Mathematical and theoretical · 2018Article
- Uniform Contraction-Expansion Description of Relative Centromere and Telomere Motion.Biophysical journal · 2015Article
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5 authors.
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Abstract
Accurately characterizing the anomalous diffusion of a tracer particle has become a central issue in biophysics. However, measurement errors raise difficulty in the characterization of single trajectories, which is usually performed through the time-averaged mean square displacement (TAMSD). In this paper, we study a fractionally integrated moving average (FIMA) process as an appropriate model for anomalous diffusion data with measurement errors. We compare FIMA and traditional TAMSD estimators for the anomalous diffusion exponent. The ability of the FIMA framework to characterize dynamics in a wide range of anomalous exponents and noise levels through the simulation of a toy model (fractional Brownian motion disturbed by Gaussian white noise) is discussed. Comparison to the TAMSD technique, shows that FIMA estimation is superior in many scenarios. This is expected to enable new measurement regimes for single particle tracking (SPT) experiments even in the presence of high measurement errors.
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