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
TLDR
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.About:
This article is published in Biophysical Journal.The article was published on 2019-07-23 and is currently open access. It has received 107 citations till now. The article focuses on the topics: Anomalous diffusion & Diffusion process.read more
Citations
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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
Measurement of anomalous diffusion using recurrent neural networks.
TL;DR: Recurrent neural networks can efficiently characterize anomalous diffusion by determining the exponent from a single short trajectory, outperforming the standard estimation based on the MSD when the available data points are limited, as is often the case in experiments.
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
DeepMoD: Deep learning for model discovery in noisy data
TL;DR: DeepMoD as discussed by the authors discovers the partial differential equation underlying a spatio-temporal data set using sparse regression on a library of possible functions and their derivatives, using a neural network as function approximator and its output to construct the function library, allowing to perform the sparse regression within the neural network.
Journal ArticleDOI
Particle tracking of nanoparticles in soft matter
TL;DR: In this article, the authors provide a basic understanding of particle tracking instrumentation, the fundamentals of tracking analysis, and potential sources of error and bias inherent in analyzing particle tracking, as well as a brief outlook for the future particle tracking through the lens of machine learning.
References
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Fiji: an open-source platform for biological-image analysis
Johannes Schindelin,Ignacio Arganda-Carreras,Erwin Frise,Verena Kaynig,Mark Longair,Tobias Pietzsch,Stephan Preibisch,Curtis Rueden,Stephan Saalfeld,Benjamin Schmid,Jean-Yves Tinevez,Daniel J. White,Volker Hartenstein,Kevin W. Eliceiri,Pavel Tomancak,Albert Cardona +15 more
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TL;DR: This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%.