Work

Feature Selection and Hyperparameter Optimization for Machine Learned Classification of 3D Single-Particle Tracking

Year: 2025

Type: article

Abstract: Understanding diffusion in charged and crowded media is crucial for solving a wide range of biological and materials challenges. Classifying diffusion by traditional methods such as mean square displacement in three-dimensional single-particle tracking (3D SPT) is difficult, especially when there are mixed motion types. To address this, we employed machine learning (ML), specifically decision tree algorithms with feature selection, to identify the six most relevant features for accurate characterization of trajectories. This work demonstrates the value of ML in advancing our understanding of heterogeneous transport that occurs in charged and crowded environments, with a broad range of applications.

Source: Chemical & Biomedical Imaging

DOI: 10.1021/cbmi.5c00057

Authors: Jagriti Chatterjee, Subhojyoti Chatterjee, Emil Gillett, Nikita Kovalenko, Dongyu Fan, Christy F. Landes

Institutions: University of Illinois Urbana-Champaign, Rice University

Language: English

Topic: Fractional Differential Equations Solutions

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: Rice University, University of Illinois at Urbana-Champaign

Award: G4314815125