274 related articles for article (PubMed ID: 20840736)
1. Parameter optimization by using differential elimination: a general approach for introducing constraints into objective functions.
Nakatsui M; Horimoto K; Okamoto M; Tokumoto Y; Miyake J
BMC Syst Biol; 2010 Sep; 4 Suppl 2(Suppl 2):S9. PubMed ID: 20840736
[TBL] [Abstract][Full Text] [Related]
2. Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.
Tashkova K; Korošec P; Silc J; Todorovski L; Džeroski S
BMC Syst Biol; 2011 Oct; 5():159. PubMed ID: 21989196
[TBL] [Abstract][Full Text] [Related]
3. An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways.
Ismail AM; Mohamad MS; Abdul Majid H; Abas KH; Deris S; Zaki N; Mohd Hashim SZ; Ibrahim Z; Remli MA
Biosystems; 2017 Dec; 162():81-89. PubMed ID: 28951204
[TBL] [Abstract][Full Text] [Related]
4. An improved swarm optimization for parameter estimation and biological model selection.
Abdullah A; Deris S; Mohamad MS; Anwar S
PLoS One; 2013; 8(4):e61258. PubMed ID: 23593445
[TBL] [Abstract][Full Text] [Related]
5. Brute force meets Bruno force in parameter optimisation: introduction of novel constraints for parameter accuracy improvement by symbolic computation.
Nakatsui M; Horimoto K; Lemaire F; Ürgüplü A; Sedoglavic A; Boulier F
IET Syst Biol; 2011 Sep; 5(5):281-92. PubMed ID: 22010755
[TBL] [Abstract][Full Text] [Related]
6. Extraction of elementary rate constants from global network analysis of E. coli central metabolism.
Zhao J; Ridgway D; Broderick G; Kovalenko A; Ellison M
BMC Syst Biol; 2008 May; 2():41. PubMed ID: 18462493
[TBL] [Abstract][Full Text] [Related]
7. Biochemical systems identification by a random drift particle swarm optimization approach.
Sun J; Palade V; Cai Y; Fang W; Wu X
BMC Bioinformatics; 2014; 15 Suppl 6(Suppl 6):S1. PubMed ID: 25078435
[TBL] [Abstract][Full Text] [Related]
8. Parameter estimation for stiff equations of biosystems using radial basis function networks.
Matsubara Y; Kikuchi S; Sugimoto M; Tomita M
BMC Bioinformatics; 2006 Apr; 7():230. PubMed ID: 16643665
[TBL] [Abstract][Full Text] [Related]
9. Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks.
Dai Z; Lai L
Mol Biosyst; 2014 Jun; 10(6):1385-92. PubMed ID: 24714701
[TBL] [Abstract][Full Text] [Related]
10. MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling.
Maeda K; Hatae A; Sakai Y; Boogerd FC; Kurata H
BMC Bioinformatics; 2022 Nov; 23(1):455. PubMed ID: 36319952
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models.
Zeng N; Wang Z; Li Y; Du M; Liu X
IEEE/ACM Trans Comput Biol Bioinform; 2012; 9(2):321-9. PubMed ID: 22025755
[TBL] [Abstract][Full Text] [Related]
13. Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints.
Fiedler A; Raeth S; Theis FJ; Hausser A; Hasenauer J
BMC Syst Biol; 2016 Aug; 10(1):80. PubMed ID: 27549154
[TBL] [Abstract][Full Text] [Related]
14. An improved constraint filtering technique for inferring hidden states and parameters of a biological model.
Murtuza Baker S; Poskar CH; Schreiber F; Junker BH
Bioinformatics; 2013 Apr; 29(8):1052-9. PubMed ID: 23434837
[TBL] [Abstract][Full Text] [Related]
15. LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models.
Tian X; Pang W; Wang Y; Guo K; Zhou Y
Biosystems; 2019 Aug; 182():8-16. PubMed ID: 31167112
[TBL] [Abstract][Full Text] [Related]
16. Efficient Methods for Parameter Estimation of Ordinary and Partial Differential Equation Models of Viral Hepatitis Kinetics.
Churkin A; Lewkiewicz S; Reinharz V; Dahari H; Barash D
Mathematics (Basel); 2020 Sep; 8(9):. PubMed ID: 33224865
[TBL] [Abstract][Full Text] [Related]
17. Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training.
Meissner M; Schmuker M; Schneider G
BMC Bioinformatics; 2006 Mar; 7():125. PubMed ID: 16529661
[TBL] [Abstract][Full Text] [Related]
18. Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies.
Dräger A; Kronfeld M; Ziller MJ; Supper J; Planatscher H; Magnus JB; Oldiges M; Kohlbacher O; Zell A
BMC Syst Biol; 2009 Jan; 3():5. PubMed ID: 19144170
[TBL] [Abstract][Full Text] [Related]
19. Bayesian parameter estimation for nonlinear modelling of biological pathways.
Ghasemi O; Lindsey ML; Yang T; Nguyen N; Huang Y; Jin YF
BMC Syst Biol; 2011; 5 Suppl 3(Suppl 3):S9. PubMed ID: 22784628
[TBL] [Abstract][Full Text] [Related]
20. A Parameter Estimation Method for Biological Systems modelled by ODE/DDE Models Using Spline Approximation and Differential Evolution Algorithm.
Zhan C; Situ W; Yeung LF; Tsang PW; Yang G
IEEE/ACM Trans Comput Biol Bioinform; 2014; 11(6):1066-76. PubMed ID: 26357044
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
