Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories

TL;DR

This paper reviews recent machine-learning methods for analyzing single-particle diffusion trajectories, emphasizing interpretability, uncertainty estimation, and robustness to out-of-distribution data, to better understand diffusive systems.

Contribution

It provides an overview of state-of-the-art machine-learning techniques for diffusive time series, highlighting interpretability and uncertainty estimation improvements.

Findings

Machine-learning methods outperform traditional techniques in classifying diffusion types.

Inclusion of uncertainty estimates enhances interpretability of models.

Models show robustness when applied to out-of-distribution data.

Abstract

Single-particle traces of the diffusive motion of molecules, cells, or animals are by-now routinely measured, similar to stochastic records of stock prices or weather data. Deciphering the stochastic mechanism behind the recorded dynamics is vital in understanding the observed systems. Typically, the task is to decipher the exact type of diffusion and/or to determine system parameters. The tools used in this endeavor are currently revolutionized by modern machine-learning techniques. In this Perspective we provide an overview over recently introduced methods in machine-learning for diffusive time series, most notably, those successfully competing in the Anomalous-Diffusion-Challenge. As such methods are often criticized for their lack of interpretability, we focus on means to include uncertainty estimates and feature-based approaches, both improving interpretability and providing concrete insight into the learning process of the machine. We expand the discussion by examining predictions on different out-of-distribution data. We also comment on expected future developments.