2020/05/31 by Joanna Janczura, Patrycja Kowalek, Hanna Loch-Olszewska +2 · 73 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Boosting (machine learning) #Computer science #Diffusion #Displacement (psychology) #Fractional Differential Equations Solutions #Gradient boosting #Identification (biology) #Lipid Membrane Structure and Behavior #Machine learning #Mean squared displacement #Physics #Random forest #Set (abstract data type) #Support vector machine #physics.bio-ph #q-bio.QM #stochastic dynamics and bifurcation
paper · pdf · doi:10.1103/physreve.102.032402
published in Physical review. E 102(3), 032402 (American Physical Society) · 32 pages, 5 figures
arxiv created 2020/07/10 · openalex publication_date 2020/09/01 · arxiv updated 2020/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Single-particle tracking (SPT) has become a popular tool to study the intracellular transport of molecules in living cells. Inferring the character of their dynamics is important, because it determines the organization and functions of the cells. For this reason, one of the first steps in the analysis of SPT data is the identification of the diffusion type of the observed particles. The most popular method to identify the class of a trajectory is based on the mean-square displacement (MSD). However, due to its known limitations, several other approaches have been already proposed. With the recent advances in algorithms and the developments of modern hardware, the classification attempts rooted in machine learning (ML) are of particular interest. In this work, we adopt two ML ensemble algorithms, i.e., random forest and gradient boosting, to the problem of trajectory classification. We present a new set of features used to transform the raw trajectories data into input vectors required by the classifiers. The resulting models are then applied to real data for G protein-coupled receptors and G proteins. The classification results are compared to recent statistical methods going beyond MSD.