ArticleBMC bioinformatics2016
Automatic detection of diffusion modes within biological membranes using back-propagation neural network.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research.Computational and mathematical methods in medicine · 2021Pooled it
- Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data.Cells · 2026Review
- Lipopolysaccharide confinement in the bacterial outer membrane is governed by interactions within the conserved Lipid A anchor.The EMBO journal · 2026Article
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Pointwise prediction of protein diffusive properties using machine learning.JPhys photonics · 2025Article
- Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function.Nature methods · 2025Article
- Quantifying surface tension and viscosity in biomolecular condensates by FRAP-ID.Biophysical journal · 2024Article
- Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.International journal of molecular sciences · 2024Review
- Computational drug development for membrane protein targets.Nature biotechnology · 2024Review
- Single-molecule tracking to determine the abundances and stoichiometries of freely-diffusing protein complexes in living cells: Past applications and future prospects.The Journal of chemical physics · 2023Review
- Recent Advances in Single-Molecule Tracking and Imaging Techniques.Annual review of analytical chemistry (Palo Alto, Calif.) · 2023Review
- The Utility of Fluorescence Recovery after Photobleaching (FRAP) to Study the Plasma Membrane.Membranes · 2023Review
- Classification-based motion analysis of single-molecule trajectories using DiffusionLab.Scientific reports · 2022Article
- Detecting Transient Trapping from a Single Trajectory: A Structural Approach.Entropy (Basel, Switzerland) · 2021Article
- CD82 and Gangliosides Tune CD81 Membrane Behavior.International journal of molecular sciences · 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
- Fractional Dynamics Identification via Intelligent Unpacking of the Sample Autocovariance Function by Neural Networks.Entropy (Basel, Switzerland) · 2020Article
- Recent advances in point spread function engineering and related computational microscopy approaches: from one viewpoint.Biophysical reviews · 2020Review
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle particle tracking (SPT) is nowadays one of the most popular technique to probe spatio-temporal dynamics of proteins diffusing within the plasma membrane. Indeed membrane components of eukaryotic cells are very dynamic molecules and can diffuse according to different motion modes. Trajectories are often reconstructed frame-by-frame and dynamic properties often evaluated using mean square displacement (MSD) analysis. However, to get statistically significant results in tracking experiments, analysis of a large number of trajectories is required and new methods facilitating this analysis are still needed.
resultsIn this study we developed a new algorithm based on back-propagation neural network (BPNN) and MSD analysis using a sliding window. The neural network was trained and cross validated with short synthetic trajectories. For simulated and experimental data, the algorithm was shown to accurately discriminate between Brownian, confined and directed diffusion modes within one trajectory, the 3 main of diffusion encountered for proteins diffusing within biological membranes. It does not require a minimum number of observed particle displacements within the trajectory to infer the presence of multiple motion states. The size of the sliding window was small enough to measure local behavior and to detect switches between different diffusion modes for segments as short as 20 frames. It also provides quantitative information from each segment of these trajectories. Besides its ability to detect switches between 3 modes of diffusion, this algorithm is able to analyze simultaneously hundreds of trajectories with a short computational time.
conclusionThis new algorithm, implemented in powerful and handy software, provides a new conceptual and versatile tool, to accurately analyze the dynamic behavior of membrane components.
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