Machine learning method for single trajectory characterization

Abstract

In order to study transport in complex environments, it is extremely important to determine the physical mechanism underlying diffusion, and precisely characterize its nature and parameters. Often, this task is strongly impacted by data consisting of trajectories with short length and limited localization precision. In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate even very short trajectories to the underlying diffusion mechanism with a high accuracy. In addition, the method is able to classify the motion according to normal or anomalous diffusion, and determine its anomalous exponent with a small error. The method provides highly accurate outputs even when working with very short trajectories and in the presence of experimental noise. We further demonstrate the application of transfer learning to experimental and simulated data not included in the training/testing dataset. This allows for a full, high-accuracy characterization of experimental trajectories without the need of any prior information.

Complementary code can be found in https://github.com/gorkamunoz/RF-Single-Trajectory-Characterization

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Cited by in corpus (32)

• Bayesian deep learning for error estimation in the analysis of anomalous diffusion
• Classification of particle trajectories in living cells: machine learning versus statistical testing hypothesis for fractional anomalous diffusion
• General Defocusing Particle Tracking: fundamentals and uncertainty assessment
• Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
• Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories
• Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)
• Boosting the performance of anomalous diffusion classifiers with the proper choice of features
• Inferring pointwise diffusion properties of single trajectories with deep learning
• Quantitative evaluation of methods to analyze motion changes in single-particle experiments
• Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories
• Characterization of anomalous diffusion through convolutional transformers
• Modelling intermittent anomalous diffusion with switching fractional Brownian motion
• Different anomalous diffusion regimes measured in the dynamics of tracer particles in actin networks
• Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories (AnDi-ELM)
• Unsupervised learning of anomalous diffusion data
• WaveNet-Based Deep Neural Networks for the Characterization of Anomalous Diffusion (WADNet)
• Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories
• Inference of Markov models from trajectories via Large Deviations at Level 2.5 with applications to random walks in disordered media
• Anomalous diffusion, non-Gaussianity, and nonergodicity for subordinated fractional Brownian motion with a drift
• Machine Learning Analysis of Anomalous Diffusion
• Objective comparison of methods to decode anomalous diffusion
• Preface: Characterisation of Physical Processes from Anomalous Diffusion Data
• Anomalous statistics in the Langevin equation with fluctuating diffusivity: from Brownian yet non-Gaussian diffusion to anomalous diffusion and ergodicity breaking
• Change-point detection in anomalous-diffusion trajectories utilising machine-learning-based uncertainty estimates
• Variable-order fractional master equation and clustering of particles: non-uniform lysosome distribution
• Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories
• Shape matters: Inferring the motility of confluent cells from static images
• Bayesian inference of Lévy walks via hidden Markov models
• Recurrent neural network analysis of single trajectories switching between anomalous diffusion states
• Classification of anomalous diffusion in animal movement data using power spectral analysis
• Improved mean squared displacement analysis for anomalous single particle trajectories
• Machine Learning Forecasting of Active Nematics