151 related articles for article (PubMed ID: 26317784)
1. Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.
Schillings C; Sunnåker M; Stelling J; Schwab C
PLoS Comput Biol; 2015 Aug; 11(8):e1004457. PubMed ID: 26317784
[TBL] [Abstract][Full Text] [Related]
2. Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent.
Wang Y; Christley S; Mjolsness E; Xie X
BMC Syst Biol; 2010 Jul; 4():99. PubMed ID: 20663171
[TBL] [Abstract][Full Text] [Related]
3. Efficient characterization of high-dimensional parameter spaces for systems biology.
Zamora-Sillero E; Hafner M; Ibig A; Stelling J; Wagner A
BMC Syst Biol; 2011 Sep; 5():142. PubMed ID: 21920040
[TBL] [Abstract][Full Text] [Related]
4. Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties.
Miskovic L; Béal J; Moret M; Hatzimanikatis V
PLoS Comput Biol; 2019 Aug; 15(8):e1007242. PubMed ID: 31430276
[TBL] [Abstract][Full Text] [Related]
5. High-dimensional Bayesian parameter estimation: case study for a model of JAK2/STAT5 signaling.
Hug S; Raue A; Hasenauer J; Bachmann J; Klingmüller U; Timmer J; Theis FJ
Math Biosci; 2013 Dec; 246(2):293-304. PubMed ID: 23602931
[TBL] [Abstract][Full Text] [Related]
6. Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems.
Kramer A; Calderhead B; Radde N
BMC Bioinformatics; 2014 Jul; 15(1):253. PubMed ID: 25066046
[TBL] [Abstract][Full Text] [Related]
7. Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems.
Ballnus B; Hug S; Hatz K; Görlitz L; Hasenauer J; Theis FJ
BMC Syst Biol; 2017 Jun; 11(1):63. PubMed ID: 28646868
[TBL] [Abstract][Full Text] [Related]
8. Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.
Penas DR; González P; Egea JA; Doallo R; Banga JR
BMC Bioinformatics; 2017 Jan; 18(1):52. PubMed ID: 28109249
[TBL] [Abstract][Full Text] [Related]
9. Use of randomized sampling for analysis of metabolic networks.
Schellenberger J; Palsson BØ
J Biol Chem; 2009 Feb; 284(9):5457-61. PubMed ID: 18940807
[TBL] [Abstract][Full Text] [Related]
10. Inference of regulatory networks with a convergence improved MCMC sampler.
Agostinho NB; Machado KS; Werhli AV
BMC Bioinformatics; 2015 Sep; 16():306. PubMed ID: 26399857
[TBL] [Abstract][Full Text] [Related]
11. Global parameter estimation methods for stochastic biochemical systems.
Poovathingal SK; Gunawan R
BMC Bioinformatics; 2010 Aug; 11():414. PubMed ID: 20691037
[TBL] [Abstract][Full Text] [Related]
12. Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory.
Pantazis Y; Katsoulakis MA; Vlachos DG
BMC Bioinformatics; 2013 Oct; 14():311. PubMed ID: 24148216
[TBL] [Abstract][Full Text] [Related]
13. Parameter estimation in models of biological oscillators: an automated regularised estimation approach.
Pitt JA; Banga JR
BMC Bioinformatics; 2019 Feb; 20(1):82. PubMed ID: 30770736
[TBL] [Abstract][Full Text] [Related]
14. Nested sampling for parameter inference in systems biology: application to an exemplar circadian model.
Aitken S; Akman OE
BMC Syst Biol; 2013 Jul; 7():72. PubMed ID: 23899119
[TBL] [Abstract][Full Text] [Related]
15. Efficient classification of complete parameter regions based on semidefinite programming.
Kuepfer L; Sauer U; Parrilo PA
BMC Bioinformatics; 2007 Jan; 8():12. PubMed ID: 17224043
[TBL] [Abstract][Full Text] [Related]
16. Global parameter identification of stochastic reaction networks from single trajectories.
Müller CL; Ramaswamy R; Sbalzarini IF
Adv Exp Med Biol; 2012; 736():477-98. PubMed ID: 22161347
[TBL] [Abstract][Full Text] [Related]
17. Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment.
Wierling C; Kühn A; Hache H; Daskalaki A; Maschke-Dutz E; Peycheva S; Li J; Herwig R; Lehrach H
Mutat Res; 2012 Aug; 746(2):163-70. PubMed ID: 22285941
[TBL] [Abstract][Full Text] [Related]
18. Bayesian parameter estimation for dynamical models in systems biology.
Linden NJ; Kramer B; Rangamani P
PLoS Comput Biol; 2022 Oct; 18(10):e1010651. PubMed ID: 36269772
[TBL] [Abstract][Full Text] [Related]
19. A methodology for performing global uncertainty and sensitivity analysis in systems biology.
Marino S; Hogue IB; Ray CJ; Kirschner DE
J Theor Biol; 2008 Sep; 254(1):178-96. PubMed ID: 18572196
[TBL] [Abstract][Full Text] [Related]
20. Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.
Tøndel K; Indahl UG; Gjuvsland AB; Vik JO; Hunter P; Omholt SW; Martens H
BMC Syst Biol; 2011 Jun; 5():90. PubMed ID: 21627852
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
