Objective comparison of methods to decode anomalous diffusion

Munoz-Gil G, Volpe G, Garcia-March MA, Aghion E, Argun A, Hong CB, Bland T, Bo S, Conejero JA, Firbas N, Orts O, Gentili A, Huang Z, Jeon JH, Kabbech H, Kim Y, Kowalek P, Krapf D, Loch-Olszewska H, Lomholt MA, Masson JB, Meyer PG, Park S, Requena B, Smal I, Song T, Szwabinski J, Thapa S, Verdier H, Volpe G, Widera A, Lewenstein M, Metzler R, Manzo C (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Book Volume: 12

Article Number: 6253

Journal Issue: 1

DOI: 10.1038/s41467-021-26320-w

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.

Involved external institutions

Université de Paris France (FR) Technische Universität Kaiserslautern Germany (DE) Institució Catalana de Recerca i Estudis Avançats (ICREA) / Catalan Institution for Research and Advanced Studies Spain (ES) Universität Potsdam Germany (DE) Autonomous University of Barcelona (UAB) / Universitat Autònoma de Barcelona Spain (ES) University of Gothenburg / Göteborgs universitet Sweden (SE) Polytechnic University of Valencia / Universidad Politécnica de Valencia Spain (ES) Max-Planck-Institut für Physik komplexer Systeme (MPI PKS) / Max Planck Institute for the Physics of Complex Systems Germany (DE) Pohang University of Science and Technology (POSTECH) Korea, Republic of (KR) The Francis Crick Institute United Kingdom (GB) University College London (UCL) United Kingdom (GB) Hunan University / Húnán Dàxué China (CN) Erasmus University Medical Center (MC) Netherlands (NL) Wrocław University of Science and Technology / Politechnika Wrocławska Poland (PL) Colorado State University United States (USA) (US) University of Southern Denmark / Syddansk Universitet Denmark (DK) National Center for Scientific Research / Centre national de la recherche scientifique (CNRS) France (FR)

How to cite

APA:

Munoz-Gil, G., Volpe, G., Garcia-March, M.A., Aghion, E., Argun, A., Hong, C.B.,... Manzo, C. (2021). Objective comparison of methods to decode anomalous diffusion. Nature Communications, 12(1). https://dx.doi.org/10.1038/s41467-021-26320-w

MLA:

Munoz-Gil, Gorka, et al. "Objective comparison of methods to decode anomalous diffusion." Nature Communications 12.1 (2021).

BibTeX: Download