An anomalous competition: assessment of methods for anomalous diffusion through a community effort (Conference Presentation)

Carlo Manzo; Giovanni Volpe

doi:10.1117/12.2633880

4 October 2022 An anomalous competition: assessment of methods for anomalous diffusion through a community effort (Conference Presentation)

Carlo Manzo, Giovanni Volpe

Author Affiliations +

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC1220412 (2022) https://doi.org/10.1117/12.2633880
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Deviations from the law of Brownian motion, typically referred to as anomalous diffusion, are ubiquitous in science and associated with non-equilibrium phenomena, flows of energy and information, and transport in living systems. In the last years, the booming of machine learning has boosted the development of new methods to detect and characterize anomalous diffusion from individual trajectories, going beyond classical calculations based on the mean squared displacement. We thus designed the AnDi challenge, an open community effort to objectively assess the performance of conventional and novel methods. We developed a python library for generating simulated datasets according to the most popular theoretical models of diffusion. We evaluated 16 methods over 3 different tasks and 3 different dimensions, involving anomalous exponent inference, model classification, and trajectory segmentation. Our analysis provides the first assessment of methods for anomalous diffusion in a variety of realistic conditions of trajectory length and noise. Furthermore, we compared the prediction provided by these methods for several experimental datasets. The results of this study further highlight the role that anomalous diffusion has in defining the biological function while revealing insight into the current state of the field and providing a benchmark for future developers.

Conference Presentation

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Carlo Manzo and Giovanni Volpe "An anomalous competition: assessment of methods for anomalous diffusion through a community effort (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC1220412 (4 October 2022); https://doi.org/10.1117/12.2633880

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KEYWORDS

Diffusion

Biological research

Computer simulations

Data modeling

Library classification systems

Machine learning

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