Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.

Area of Science:

• Biophysics
• Cell Biology
• Computational Biology
• Machine Learning Applications

Background:

• Single-particle tracking (SPT) offers high-resolution insights into molecular dynamics within living cells.
• Traditional SPT analysis methods struggle with biological data complexity, noise, and heterogeneity.
• Existing pipelines often require manual adjustments or rely on oversimplified models.

Purpose of the Study:

• To systematically review the impact of machine learning (ML) and deep learning (DL) on single-particle tracking (SPT) workflows.
• To assess how ML/DL techniques enhance accuracy, robustness, and interpretability in SPT data analysis.
• To provide a guide to current ML in SPT, evaluating state-of-the-art methods and future directions.

Main Methods:

• Survey of ML/DL techniques including convolutional neural networks (CNNs), recurrent architectures, and Bayesian deep learning.
• Evaluation of ML/DL applications in particle detection, trajectory linking, motion classification, denoising, and biophysical inference.
• Discussion of practical considerations for ML deployment, including data set construction and problem domain selection.

Main Results:

• ML/DL methods demonstrably improve the accuracy and robustness of SPT analyses.
• Deep learning approaches effectively address challenges posed by noisy and heterogeneous biological data.
• ML/DL facilitates more sophisticated biophysical inference and reveals novel biological insights from SPT data.

Conclusions:

• Machine learning, particularly deep learning, represents a transformative advancement in single-particle tracking analysis.
• These computational tools overcome limitations of traditional methods, enabling deeper understanding of cellular dynamics.
• The review highlights the potential for ML/DL to drive future discoveries in molecular and cellular biology.