Published December 11, 2020
| Version v1.1
Software
Open
Anomalous diffusion classification - three sets of features for the feature-based machine learning approach
Creators
- 1. Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wrocław University of Science and Technology
Contributors
Related person:
Researcher:
Supervisor:
- 1. Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wrocław University of Science and Technology
Description
The anomalous diffusion classification classifiers used in: H. Loch-Olszewska and J. Szwabiński "Impact of feature choice on machine learning classification of fractional anomalous diffusion". Manuscript in preparation.
Files
HannaLochOlszewska/FAD_classification-v1.1.zip
Files
(22.6 kB)
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Additional details
Related works
- Is supplement to
- https://github.com/HannaLochOlszewska/FAD_classification/tree/v1.1 (URL)
References
- Kowalek, P.; Loch-Olszewska, H.; Szwabiński, J. Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach. Phys. Rev. E 2019, 100, 032410. doi:10.1103/PhysRevE.100.032410.
- Janczura, J.; Kowalek, P.; Loch-Olszewska, H.; Szwabiński, J.;Weron, A. Classification of particle trajectories in living cells: Machine learning versus statistical testing hypothesis for fractional anomalous diffusion. Phys. Rev. E 2020, 102, 032402. doi:10.1103/PhysRevE.102.032402.
- Wagner, T.; Kroll, A.; Haramagatti, C.R.; Lipinski, H.G.; Wiemann, M. Classification and Segmentation of Nanoparticle Diffusion Trajectories in Cellular Micro Environments. PLoS ONE 2017, 12(1), e0170165. doi:10.1371/journal.pone.0170165.