Detalhe da pesquisa
1.
Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion.
Entropy (Basel)
; 22(12)2020 Dec 19.
Artigo
em Inglês
| MEDLINE | ID: mdl-33352694
2.
Inhomogeneous membrane receptor diffusion explained by a fractional heteroscedastic time series model.
Phys Chem Chem Phys
; 21(6): 3114-3121, 2019 Feb 06.
Artigo
em Inglês
| MEDLINE | ID: mdl-30672913
3.
Detection of ε-ergodicity breaking in experimental data-A study of the dynamical functional sensibility.
J Chem Phys
; 148(20): 204105, 2018 May 28.
Artigo
em Inglês
| MEDLINE | ID: mdl-29865829
4.
Objective comparison of methods to decode anomalous diffusion.
Nat Commun
; 12(1): 6253, 2021 10 29.
Artigo
em Inglês
| MEDLINE | ID: mdl-34716305
5.
Classification of particle trajectories in living cells: Machine learning versus statistical testing hypothesis for fractional anomalous diffusion.
Phys Rev E
; 102(3-1): 032402, 2020 Sep.
Artigo
em Inglês
| MEDLINE | ID: mdl-33076015
6.
Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach.
Phys Rev E
; 100(3-1): 032410, 2019 Sep.
Artigo
em Inglês
| MEDLINE | ID: mdl-31640019
7.
Identifying ergodicity breaking for fractional anomalous diffusion: Criteria for minimal trajectory length.
Phys Rev E
; 94(5-1): 052136, 2016 Nov.
Artigo
em Inglês
| MEDLINE | ID: mdl-27967179