Objective comparison of methods to decode anomalous diffusion

Gorka Muñoz-Gil, Giovanni Volpe (), Miguel Angel Garcia-March, Erez Aghion, Aykut Argun, Chang Beom Hong, Tom Bland, Stefano Bo, J. Alberto Conejero, Nicolás 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, Janusz Szwabiński, Samudrajit Thapa, Hippolyte Verdier, Giorgio Volpe, Artur Widera, Maciej Lewenstein, Ralf Metzler and Carlo Manzo ()
Additional contact information
Gorka Muñoz-Gil: The Barcelona Institute of Science and Technology
Giovanni Volpe: University of Gothenburg
Miguel Angel Garcia-March: Universitat Politècnica de València
Erez Aghion: Max Planck Institute for the Physics of Complex Systems
Aykut Argun: University of Gothenburg
Chang Beom Hong: Pohang University of Science and Technology
Tom Bland: The Francis Crick Institute
Stefano Bo: Max Planck Institute for the Physics of Complex Systems
J. Alberto Conejero: Universitat Politècnica de València
Nicolás Firbas: Universitat Politècnica de València
Òscar Garibo i Orts: Universitat Politècnica de València
Alessia Gentili: University College London
Zihan Huang: Hunan University
Jae-Hyung Jeon: Pohang University of Science and Technology
Hélène Kabbech: Erasmus University Medical Center
Yeongjin Kim: Pohang University of Science and Technology
Patrycja Kowalek: Wrocław University of Science and Technology
Diego Krapf: Colorado State University
Hanna Loch-Olszewska: Wrocław University of Science and Technology
Michael A. Lomholt: University of Southern Denmark
Jean-Baptiste Masson: Institut Pasteur, Université de Paris, USR 3756 (C3BI/DBC) & Neuroscience department CNRS UMR 3751, Decision and Bayesian Computation lab
Philipp G. Meyer: Max Planck Institute for the Physics of Complex Systems
Seongyu Park: Pohang University of Science and Technology
Borja Requena: The Barcelona Institute of Science and Technology
Ihor Smal: Erasmus University Medical Center
Taegeun Song: Pohang University of Science and Technology
Janusz Szwabiński: Wrocław University of Science and Technology
Samudrajit Thapa: University of Potsdam
Hippolyte Verdier: Institut Pasteur, Université de Paris, USR 3756 (C3BI/DBC) & Neuroscience department CNRS UMR 3751, Decision and Bayesian Computation lab
Giorgio Volpe: University College London
Artur Widera: Technische Universität Kaiserslautern
Maciej Lewenstein: The Barcelona Institute of Science and Technology
Ralf Metzler: University of Potsdam
Carlo Manzo: The Barcelona Institute of Science and Technology

Nature Communications, 2021, vol. 12, issue 1, 1-16

Abstract: Abstract Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Date: 2021
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Citations: View citations in EconPapers (8)

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DOI: 10.1038/s41467-021-26320-w

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