Jump to main content
Jump to site search
Access to RSC content Close the message box

Continue to access RSC content when you are not at your institution. Follow our step-by-step guide.


Issue 37, 2018
Previous Article Next Article

Estimation of diffusive states from single-particle trajectory in heterogeneous medium using machine-learning methods

Author affiliations

Abstract

We propose a novel approach to analyze random walks in heterogeneous medium using a hybrid machine-learning method based on a gamma mixture and a hidden Markov model. A gamma mixture and a hidden Markov model respectively provide the number and the most probable sequence of diffusive states from the time series position data of particles/molecules obtained by single-particle/molecule tracking (SPT/SMT) method. We evaluate the performance of our proposed method for numerically generated trajectories. It is shown that our proposed method can correctly extract the number of diffusive states when each trajectory is long enough to be frame averaged. We also indicate that our method can provide an indicator whether the assumption of a medium consisting of discrete diffusive states is appropriate or not based on the available amount of trajectory data. Then, we demonstrate an application of our method to the analysis of experimentally obtained SPT data.

Graphical abstract: Estimation of diffusive states from single-particle trajectory in heterogeneous medium using machine-learning methods

Back to tab navigation

Article information


Submitted
23 Apr 2018
Accepted
24 Aug 2018
First published
11 Sep 2018

This article is Open Access

Phys. Chem. Chem. Phys., 2018,20, 24099-24108
Article type
Paper

Estimation of diffusive states from single-particle trajectory in heterogeneous medium using machine-learning methods

Y. Matsuda, I. Hanasaki, R. Iwao, H. Yamaguchi and T. Niimi, Phys. Chem. Chem. Phys., 2018, 20, 24099
DOI: 10.1039/C8CP02566E

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes.

Reproduced material should be attributed as follows:

  • For reproduction of material from NJC:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
  • For reproduction of material from PCCP:
    [Original citation] - Published by the PCCP Owner Societies.
  • For reproduction of material from PPS:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
  • For reproduction of material from all other RSC journals:
    [Original citation] - Published by The Royal Society of Chemistry.

Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.


Social activity

Search articles by author

Spotlight

Advertisements