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. LomholtJean 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, Carlo Manzo*


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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.

TidsskriftNature Communications
Udgave nummer1
Antal sider16
StatusUdgivet - dec. 2021

Bibliografisk note

Funding Information:
The authors would like to thank: Paula Kowalek for the graphical illustrations; Matthias Weiss and Maria Garcia-Parajo for sharing experimental data; Daniel Adam for help with compiling the data of single-atom trajectories. G.M.-G., B.R., and M.L. acknowledge support from ERC AdG NOQIA, Agencia Estatal de Investigación "Severo Ochoa” Center of Excellence CEX2019-000910-S, Plan National FIDEUA PID2019-106901GB-I00/10.13039/ 501100011033, FPI), Fundació Privada Cellex, Fundació Mir-Puig, and from Generalitat de Catalunya (AGAUR Grant No. 2017 SGR 1341, CERCA program, QuantumCAT U16-011424, co-funded by ERDF Operational Program of Catalonia 2014-2020), MINECO-EU QUANTERA MAQS (funded by State Research Agency (AEI) PCI2019-111828-2/ 10.13039/501100011033), EU Horizon 2020 FET-OPEN OPTOLogic (Grant No 899794), and the National Science Centre, Poland-Symfonia Grant No. 2016/20/W/ST4/00314. Giov.V. and A.A. acknowledge funding from ERC StG ComplexSwimmers (Grant No. 677511) and from the Knut and Alice Wallenberg Foundation. M.A.G.-M. acknowledges funding from the Spanish Ministry of Education and Vocational Training (MEFP) through the Beatriz Galindo program 2018 (BEAGAL18/00203). R.M. acknowledges DFG grant ME 1535/12-1. Gior.V. and A.G. acknowledge sponsorship for this work by the U.S. Office of Naval Research Global (Award No. N62909-18-1-2170). Z.H. acknowledges funding from the Fundamental Research Funds for the Central Universities. J.-H.J. acknowledges NRF grants 2020R1A2C4002490 and 2017K1A1A2013241. T.B. acknowledges support by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001086), the UK Medical Research Council (FC001086), and the Wellcome Trust (FC001086), and thanks Nate Goehring for supervision and acquisition of funding. This research was funded in whole, or in part, by the Wellcome Trust (FC001086). For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. J.A.C. acknowledges support from the ALBATROSS project (National Plan for Scientific and Technical Research and Innovation 2017-2020, No. PID2019-104978RB-I00). P.K, H.L.-O. and J.S. were funded by the Polish National Science Centre (NCN-DFG Beethoven Grant No. 2016/23/G/ST1/ 04083) and acknowledge the support by the Wroclaw Centre for Networking and Supercomputing (calculations were performed using their BEM computing cluster). S.T. acknowledges the Deutscher Akademischer Austauschdienst for PhD Scholarship (DAAD Program ID 57214224) and support in the form of a Sacker postdoctoral fellowship and funding from the Pikovski-Valazzi matching scholarship (Tel Aviv University). H.K. and I.S. acknowledge funding from the Dutch Research Council (NWO) through the GENOME-TRACK project of the Building Blocks of Life research program (Project No. 737.016.014). C.M. acknowledges funding from FEDER/Ministerio de Ciencia, Innovación y Uni-versidades – Agencia Estatal de Investigación through the "Ramón y Cajal” program 2015 (Grant No. RYC-2015-17896), and the "Programa Estatal de I+D+i Orientada a los Retos de la Sociedad” (Grant No. BFU2017-85693-R); from the Generalitat de Catalunya (AGAUR Grant No. 2017SGR940). C.M. also acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU and funding from the PO FEDER of Catalonia 2014-2020 (project PECT Osona Transformació Social, Ref. 001-P-000382).

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© 2021, The Author(s).


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