REVIEW 2 cited by
Quantitative evaluation of methods to analyze motion changes in single-particle experiments
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity of transport processes and interactions between cell components. These characteristics are seen as motion changes in the particle trajectories. Despite the existence of multiple approaches to carry out this type of analysis, no objective assessment of these methods has been performed so far. Here, we report the results of a competition to characterize and rank the performance of these methods when analyzing the dynamic behavior of single molecules. To run this competition, we implemented a software library that simulates realistic data corresponding to widespread diffusion and interaction models, both in the form of trajectories and videos obtained in typical experimental conditions. The competition constitutes the first assessment of these methods, providing insights into the current limitations of the field, fostering the development of new approaches, and guiding researchers to identify optimal tools for analyzing their experiments.
Forward citations
Cited by 2 Pith papers
-
Evaluating Gaussianity of heterogeneous fractional Brownian motion
Exact kurtosis for Markovian switching fractional Brownian motion is derived and shown to detect persistent non-Gaussianity when dwell times have power-law tails.
-
Machine Learning Analysis of Anomalous Diffusion
A review that maps machine learning methods for characterizing anomalous diffusion and compares three strategies for representing diffusion trajectories.
Discussion (0). Continue with ORCID to comment.