Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)
Alessia Gentili,Giorgio Volpe +1 more
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TLDR
In this paper, a novel method (CONDOR) 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.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.read more
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Journal ArticleDOI
Objective comparison of methods to decode anomalous diffusion.
Gorka Muñoz-Gil,Giovanni Volpe,Miguel Ángel García-March,Erez Aghion,Aykut Argun,Chang Beom Hong,Tom Bland,Stefano Bo,J. Alberto Conejero,Nicolas Firbas,Òscar Garibo i Orts,Alessia Gentili,Zihan Huang,Jae-Hyung Jeon,Hélène Kabbech,Yeongjin Kim,Patrycja Kowalek,Diego Krapf,Hanna Loch-Olszewska,Michael A. Lomholt,Jean-Baptiste Masson,Philipp G. Meyer,Seongyu Park,Borja Requena,Ihor Smal,Taegeun Song,Taegeun Song,Taegeun Song,Janusz Szwabiński,Samudrajit Thapa,Samudrajit Thapa,Hippolyte Verdier,Giorgio Volpe,Artur Widera,Maciej Lewenstein,Ralf Metzler,Carlo Manzo,Carlo Manzo +37 more
TL;DR: The Anomalous Diffusion Challenge (AnDi) as mentioned in this paper was an open competition for the characterization of anomalous diffusion from the measurement of an individual trajectory, which traditionally relies on calculating the trajectory mean squared displacement.
Journal ArticleDOI
Bayesian deep learning for error estimation in the analysis of anomalous diffusion
Henrik Seckler,Ralf Metzler +1 more
TL;DR: In this article , a Bayesian-Deep-Learning technique is used to train models for both the classification of the diffusion model and the regression of the anomalous diffusion exponent of single-particle-trajectories.
Journal ArticleDOI
Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories
Journal ArticleDOI
Boosting the performance of anomalous diffusion classifiers with the proper choice of features
TL;DR: In this article , a feature-based machine learning method was developed in response to Task 2 of the Anomalous Diffusion Challenge, i.e. the classification of different types of diffusion.
Journal ArticleDOI
WaveNet-based deep neural networks for the characterization of anomalous diffusion (WADNet)
TL;DR: Wang et al. as discussed by the authors developed a WaveNet-based deep neural network (WADNet) by combining a modified WaveNet encoder with long short-term memory networks, without any prior knowledge of anomalous diffusion.
References
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Journal ArticleDOI
Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels.
TL;DR: It is concluded that-consistent with the original study-diffusion of tracers in the mucin gels is most non-Gaussian and non-ergodic at low pH that corresponds to the most heterogeneous networks.
Journal ArticleDOI
Bayesian Approach to MSD-Based Analysis of Particle Motion in Live Cells
TL;DR: This work presents a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting.
Journal ArticleDOI
Guidelines for the Fitting of Anomalous Diffusion Mean Square Displacement Graphs from Single Particle Tracking Experiments
TL;DR: This work attempts to extract practical guidelines for the estimation of anomalous time averaged MSDs through the simulation of multiple scenarios with fractional Brownian motion as a representative of a large class of fractional ergodic processes.
Journal ArticleDOI
Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data.
TL;DR: This work uses Bayesian statistics using the nested-sampling algorithm to compare and rank multiple models of ergodic diffusion as well as to assess their optimal parameters for in silico-generated and real time-series, and presents first model-ranking results in application to experimental data of tracer diffusion in polymer-based hydrogels.
Journal ArticleDOI
Single-Particle Diffusion Characterization by Deep Learning
Naor Granik,Lucien E. Weiss,Elias Nehme,Maayan Levin,Michael Chein,Eran Perlson,Yael Roichman,Yoav Shechtman +7 more
TL;DR: A neural network is implemented to classify single-particle trajectories by diffusion type: Brownian motion, fractional BrownianMotion and continuous time random walk, and the applicability of the network architecture for estimating the Hurst exponent for fractionalBrownian motion and the diffusion coefficient for Brownianmotion on both simulated and experimental data is demonstrated.