Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)

Abstract

Diffusion processes are important in several physical, chemical, biological and human phenomena. Examples include molecular encounters in reactions, cellular signalling, the foraging of animals, the spread of diseases, as well as trends in financial markets and climate records. Deviations from Brownian diffusion, known as anomalous diffusion, can often be observed in these processes, when the growth of the mean square displacement in time is not linear. An ever-increasing number of methods has thus appeared to characterize anomalous diffusion trajectories based on classical statistics or machine learning approaches. Yet, characterization of anomalous diffusion remains challenging to date as testified by the launch of the Anomalous Diffusion (AnDi) Challenge in March 2020 to assess and compare new and pre-existing methods on three different aspects of the problem: the inference of the anomalous diffusion exponent, the classification of the diffusion model, and the segmentation of trajectories. Here, we introduce a novel method (CONDOR) which combines feature engineering based on classical statistics with supervised deep learning to efficiently identify the underlying anomalous diffusion model with high accuracy and infer its exponent with a small mean absolute error in single 1D, 2D and 3D trajectories corrupted by localization noise. Finally, we extend our method to the segmentation of trajectories where the diffusion model and/or its anomalous exponent vary in time.

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Cited by in corpus (15)

• Bayesian deep learning for error estimation in the analysis of anomalous diffusion
• Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories
• Boosting the performance of anomalous diffusion classifiers with the proper choice of features
• Characterization of anomalous diffusion through convolutional transformers
• WaveNet-Based Deep Neural Networks for the Characterization of Anomalous Diffusion (WADNet)
• Unsupervised learning of anomalous diffusion data
• Semantic Segmentation of Anomalous Diffusion Using Deep Convolutional Networks
• Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories
• Machine Learning Analysis of Anomalous Diffusion
• Preface: Characterisation of Physical Processes from Anomalous Diffusion Data
• Objective comparison of methods to decode anomalous diffusion
• Change-point detection in anomalous-diffusion trajectories utilising machine-learning-based uncertainty estimates
• Bottom-up Iterative Anomalous Diffusion Detector (BI-ADD)
• Recurrent neural network analysis of single trajectories switching between anomalous diffusion states
• Recovering discrete delayed fractional equations from trajectories