Cargando…
Detection of Anomalous Diffusion with Deep Residual Networks
Identification of the diffusion type of molecules in living cells is crucial to deduct their driving forces and hence to get insight into the characteristics of the cells. In this paper, deep residual networks have been used to classify the trajectories of molecules. We started from the well known R...
Autores principales: | Gajowczyk, Miłosz, Szwabiński, Janusz |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224696/ https://www.ncbi.nlm.nih.gov/pubmed/34067344 http://dx.doi.org/10.3390/e23060649 |
Ejemplares similares
-
Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion
por: Loch-Olszewska, Hanna, et al.
Publicado: (2020) -
Objective comparison of methods to decode anomalous diffusion
por: Muñoz-Gil, Gorka, et al.
Publicado: (2021) -
Opinion Evolution in Divided Community
por: Weron, Tomasz, et al.
Publicado: (2022) -
Bayesian deep learning for error estimation in the analysis of anomalous diffusion
por: Seckler, Henrik, et al.
Publicado: (2022) -
Attribution Markers and Data Mining in Art Authentication
por: Łydżba-Kopczyńska, Barbara I., et al.
Publicado: (2021)