ArticleChemical & biomedical imaging2026
Feature Selection and Hyperparameter Optimization for Machine Learned Classification of 3D Single-Particle Tracking.
Article in Chemical & biomedical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Fused deep learning enables 6D single-molecule localization in polarization-resolved microscopy.Methods and applications in fluorescence · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.