These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
27. ConvAn: a convergence analyzing tool for optimization of biochemical networks. Kostromins A; Mozga I; Stalidzans E Biosystems; 2012; 108(1-3):73-7. PubMed ID: 22212352 [TBL] [Abstract][Full Text] [Related]
28. 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]
29. 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]
30. 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]
32. 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]
33. 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]
34. Parameter estimation in systems biology models using spline approximation. Zhan C; Yeung LF BMC Syst Biol; 2011 Jan; 5():14. PubMed ID: 21255466 [TBL] [Abstract][Full Text] [Related]
35. Scalable nonlinear programming framework for parameter estimation in dynamic biological system models. Shin S; Venturelli OS; Zavala VM PLoS Comput Biol; 2019 Mar; 15(3):e1006828. PubMed ID: 30908479 [TBL] [Abstract][Full Text] [Related]
36. A new class of wavelet networks for nonlinear system identification. Billings SA; Wei HL IEEE Trans Neural Netw; 2005 Jul; 16(4):862-74. PubMed ID: 16121728 [TBL] [Abstract][Full Text] [Related]
37. 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]
38. Identification of metabolic system parameters using global optimization methods. Polisetty PK; Voit EO; Gatzke EP Theor Biol Med Model; 2006 Jan; 3():4. PubMed ID: 16441881 [TBL] [Abstract][Full Text] [Related]
39. Realistic simulation of time-course measurements in systems biology. Egert J; Kreutz C Math Biosci Eng; 2023 Apr; 20(6):10570-10589. PubMed ID: 37322949 [TBL] [Abstract][Full Text] [Related]
40. A comparison of deterministic and stochastic approaches for sensitivity analysis in computational systems biology. Simoni G; Vo HT; Priami C; Marchetti L Brief Bioinform; 2020 Mar; 21(2):527-540. PubMed ID: 30753281 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]