Machine-learning solutions for the analysis of single-particle diffusion trajectories
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 …
routinely measured, similar to stochastic records of stock prices or weather data …
Heterogeneous anomalous transport in cellular and molecular biology
TA Waigh, N Korabel - Reports on Progress in Physics, 2023 - iopscience.iop.org
It is well established that a wide variety of phenomena in cellular and molecular biology
involve anomalous transport eg the statistics for the motility of cells and molecules are …
involve anomalous transport eg the statistics for the motility of cells and molecules are …
Objective comparison of methods to decode anomalous diffusion
Deviations from Brownian motion leading to anomalous diffusion are found in transport
dynamics from quantum physics to life sciences. The characterization of anomalous diffusion …
dynamics from quantum physics to life sciences. The characterization of anomalous diffusion …
Bayesian deep learning for error estimation in the analysis of anomalous diffusion
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion
in a wide variety of systems, from single-molecule motion in living-cells to movement …
in a wide variety of systems, from single-molecule motion in living-cells to movement …
Geometric deep learning reveals the spatiotemporal features of microscopic motion
The characterization of dynamical processes in living systems provides important clues for
their mechanistic interpretation and link to biological functions. Owing to recent advances in …
their mechanistic interpretation and link to biological functions. Owing to recent advances in …
Quantifying postsynaptic receptor dynamics: insights into synaptic function
The molecular composition of presynaptic and postsynaptic neuronal terminals is dynamic,
and yet long-term stabilizations in postsynaptic responses are necessary for synaptic …
and yet long-term stabilizations in postsynaptic responses are necessary for synaptic …
Deep learning-based parameter estimation of stochastic differential equations driven by fractional Brownian motions with measurement noise
This study proposes a general parameter estimation neural network (PENN) to jointly
identify the system parameters and the noise parameters of a stochastic differential equation …
identify the system parameters and the noise parameters of a stochastic differential equation …
Neural network-based parameter estimation of stochastic differential equations driven by Lévy noise
In this paper, a novel parameter estimation method based on a two-stage neural network
(PENN) is proposed to carry out a joint estimation of a parameterized stochastic differential …
(PENN) is proposed to carry out a joint estimation of a parameterized stochastic differential …
Inferring pointwise diffusion properties of single trajectories with deep learning
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 …
it is essential to accurately determine their diffusive properties. To do so, we propose a …
Semantic segmentation of anomalous diffusion using deep convolutional networks
X Qu, Y Hu, W Cai, Y Xu, H Ke, G Zhu, Z Huang - Physical Review Research, 2024 - APS
Heterogeneous dynamics commonly emerges in anomalous diffusion with intermittent
transitions of diffusion states but proves challenging to identify using conventional statistical …
transitions of diffusion states but proves challenging to identify using conventional statistical …