Literature DB >> 31108610

Statistical testing approach for fractional anomalous diffusion classification.

Aleksander Weron1, Joanna Janczura1, Ewa Boryczka1, Titiwat Sungkaworn2, Davide Calebiro3.   

Abstract

Taking advantage of recent single-particle tracking data, we compare the popular standard mean-squared displacement method with a statistical testing hypothesis procedure for three testing statistics and for two particle types: membrane receptors and the G proteins coupled to them. Each method results in different classifications. For this reason, more rigorous statistical tests are analyzed here in detail. The main conclusion is that the statistical testing approaches might provide good results even for short trajectories, but none of the proposed methods is "the best" for all considered examples; in other words, one needs to combine different approaches to get a reliable classification.

Entities:  

Year:  2019        PMID: 31108610     DOI: 10.1103/PhysRevE.99.042149

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  4 in total

1.  Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation.

Authors:  David Geisel; Peter Lenz
Journal:  PLoS One       Date:  2022-01-21       Impact factor: 3.240

2.  Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion.

Authors:  Hanna Loch-Olszewska; Janusz Szwabiński
Journal:  Entropy (Basel)       Date:  2020-12-19       Impact factor: 2.524

3.  Detection of Anomalous Diffusion with Deep Residual Networks.

Authors:  Miłosz Gajowczyk; Janusz Szwabiński
Journal:  Entropy (Basel)       Date:  2021-05-22       Impact factor: 2.524

4.  Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion.

Authors:  Henrik D Pinholt; Søren S-R Bohr; Josephine F Iversen; Wouter Boomsma; Nikos S Hatzakis
Journal:  Proc Natl Acad Sci U S A       Date:  2021-08-03       Impact factor: 11.205

  4 in total

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