ArticleEntropy (Basel, Switzerland)2020
Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data.Cells · 2026Review
- Exploring how deep learning decodes anomalous diffusion via Grad-CAM.Nature communications · 2026Article
- Pointwise prediction of protein diffusive properties using machine learning.JPhys photonics · 2025Article
- FADEL: Ensemble Learning Enhanced by Feature Augmentation and Discretization.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing fluorescence correlation spectroscopy with machine learning to infer anomalous molecular motion.Biophysical journal · 2025Article
- Bayesian deep learning for error estimation in the analysis of anomalous diffusion.Nature communications · 2022Article
- Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models.PeerJ. Computer science · 2022Article
- Objective comparison of methods to decode anomalous diffusion.Nature communications · 2021Article
- Detection of Anomalous Diffusion with Deep Residual Networks.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The growing interest in machine learning methods has raised the need for a careful study of their application to the experimental single-particle tracking data. In this paper, we present the differences in the classification of the fractional anomalous diffusion trajectories that arise from the selection of the features used in random forest and gradient boosting algorithms. Comparing two recently used sets of human-engineered attributes with a new one, which was tailor-made for the problem, we show the importance of a thoughtful choice of the features and parameters. We also analyse the influence of alterations of synthetic training data set on the classification results. The trained classifiers are tested on real trajectories of G proteins and their receptors on a plasma membrane.
Indexed as
Identifiers
What OpenQuestion holds
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.