Objective comparison of methods to decode anomalous diffusion

TL;DR

This paper presents an objective comparison of various methods for decoding anomalous diffusion from trajectories, highlighting the superiority of machine-learning approaches through an organized community challenge.

Contribution

It introduces the Anomalous Diffusion challenge, providing a benchmark dataset and evaluation framework for comparing different decoding methods, especially machine learning techniques.

Findings

Machine-learning methods outperform traditional approaches across scenarios.

No single method is best for all cases, indicating the need for context-specific solutions.

The challenge offers practical guidance and a benchmark for future research.

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.