Inferring pointwise diffusion properties of single trajectories with deep learning.

ABSTRACT: To characterize the mechanisms governing the diffusion of particles in biological scenarios, it is essential to accurately determine their diffusive properties. To do so, we propose a machine-learning method to characterize diffusion processes with time-dependent properties at the experimental time resolution. Our approach operates at the single-trajectory level predicting the properties of interest, such as the diffusion coefficient or the anomalous diffusion exponent, at every time step of the trajectory. In this way, changes in the diffusive properties occurring along the trajectory emerge naturally in the prediction and thus allow the characterization without any prior knowledge or assumption about the system. We first benchmark the method on synthetic trajectories simulated under seve

SUBMITTER: Requena B 

PROVIDER: S-EPMC10698275 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature