ArticleProceedings of the National Academy of Sciences of the United States of America2021
Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed, 85 citations in OpenAlex.
- Strategies for modelling the intestinal mucus interface: Focus on drug transport and host-microbiota interactions.International journal of pharmaceutics: X · 2026Review
- Fractional Physics Informed Neural Networks for Surrogate Modeling of Non-Markovian Discrete-Time Quantum Walks.Entropy (Basel, Switzerland) · 2026Article
- Graph neural network modeling of receptor interaction kinetics from single-molecule imaging data.bioRxiv : the preprint server for biology · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- 3D Single-Virus Tracking: Advances in Methodology and Labeling Strategies Towards Probing the Virus-Epithelium Interaction.Viruses · 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
- Diverse intracellular trafficking of insulin analogs by machine learning-based colocalization and diffusion analysis.iScience · 2026Article
- Bacteriophage-Mimetic DNA Origami Needle for Targeted Membrane Penetration and Cytosolic Cargo Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Feature Selection and Hyperparameter Optimization for Machine Learned Classification of 3D Single-Particle Tracking.Chemical & biomedical imaging · 2026Article
- D‑Blur: A Deep Learning Approach for Mapping Subdiffraction Diffusion with Motion-Blurred Images.Chemical & biomedical imaging · 2025Article
- Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus.Journal of nanobiotechnology · 2025Article
- Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics.JACS Au · 2025Review
- 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
- Transcription dynamics and genome organization in the mammalian nucleus: Recent advances.Molecular cell · 2025Review
- High-fidelity predictions of diffusion in the brain microenvironment.Biophysical journal · 2024Article
- Single-particle rotational microrheology enables pathological staging of macrophage foaming and antiatherosclerotic studies.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.International journal of molecular sciences · 2024Review
- Neonatal intestinal mucus barrier changes in response to maturity, inflammation, and sodium decanoate supplementation.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term "diffusional fingerprinting." This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting's utility as a universal paradigm for SPT diffusional analysis and prediction.
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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.