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

Yu Matsuda*, Itsuo Hanasaki, Ryo Iwao, Hiroki Yamaguchi, Tomohide Niimi

*この研究の対応する著者

研究成果: Article査読

10 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)24099-24108
ページ数10
ジャーナルPhysical Chemistry Chemical Physics
20
37
DOI
出版ステータスPublished - 2018

ASJC Scopus subject areas

  • 物理学および天文学(全般)
  • 物理化学および理論化学

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