ArticleScientific reports2022
Classification-based motion analysis of single-molecule trajectories using DiffusionLab.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Unraveling the Dynamics of Oxytocin in Hypothalamic Neurons.Traffic (Copenhagen, Denmark) · 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
- Feature Selection and Hyperparameter Optimization for Machine Learned Classification of 3D Single-Particle Tracking.Chemical & biomedical imaging · 2026Article
- Accurate Single-Particle Tracking and Diffusion Measurement in Freestanding Lipid Bilayers and Model Membranes.Analytical chemistry · 2025Article
- High-fidelity predictions of diffusion in the brain microenvironment.Biophysical journal · 2024Article
- Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.International journal of molecular sciences · 2024Review
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4 authors.
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Abstract
Single-particle tracking is a powerful approach to study the motion of individual molecules and particles. It can uncover heterogeneities that are invisible to ensemble techniques, which places it uniquely among techniques to study mass transport. Analysis of the trajectories obtained with single-particle tracking in inorganic porous hosts is often challenging, because trajectories are short and/or motion is heterogeneous. We present the DiffusionLab software package for motion analysis of such challenging data sets. Trajectories are first classified into populations with similar characteristics to which the motion analysis is tailored in a second step. DiffusionLab provides tools to classify trajectories based on the motion type either with machine learning or manually. It also offers quantitative mean squared displacement analysis of the trajectories. The software can compute the diffusion constant for an individual trajectory if it is sufficiently long, or the average diffusion constant for multiple shorter trajectories. We demonstrate the DiffusionLab approach via the analysis of a simulated data set with motion types frequently observed in inorganic porous hosts, such as zeolites. The software package with graphical user interface and its documentation are freely available.
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