199 related articles for article (PubMed ID: 30816929)
1. Benchmarking optimization methods for parameter estimation in large kinetic models.
Villaverde AF; Fröhlich F; Weindl D; Hasenauer J; Banga JR
Bioinformatics; 2019 Mar; 35(5):830-838. PubMed ID: 30816929
[TBL] [Abstract][Full Text] [Related]
2. Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.
Rodriguez-Fernandez M; Egea JA; Banga JR
BMC Bioinformatics; 2006 Nov; 7():483. PubMed ID: 17081289
[TBL] [Abstract][Full Text] [Related]
3. Efficient gradient-based parameter estimation for dynamic models using qualitative data.
Schmiester L; Weindl D; Hasenauer J
Bioinformatics; 2021 Dec; 37(23):4493-4500. PubMed ID: 34260697
[TBL] [Abstract][Full Text] [Related]
4. Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis.
Stapor P; Fröhlich F; Hasenauer J
Bioinformatics; 2018 Jul; 34(13):i151-i159. PubMed ID: 29949990
[TBL] [Abstract][Full Text] [Related]
5. Piecewise parameter estimation for stochastic models in COPASI.
Bergmann FT; Sahle S; Zimmer C
Bioinformatics; 2016 May; 32(10):1586-8. PubMed ID: 26787664
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Efficient parameterization of large-scale dynamic models based on relative measurements.
Schmiester L; Schälte Y; Fröhlich F; Hasenauer J; Weindl D
Bioinformatics; 2020 Jan; 36(2):594-602. PubMed ID: 31347657
[TBL] [Abstract][Full Text] [Related]
8. Benchmark problems for dynamic modeling of intracellular processes.
Hass H; Loos C; Raimúndez-Álvarez E; Timmer J; Hasenauer J; Kreutz C
Bioinformatics; 2019 Sep; 35(17):3073-3082. PubMed ID: 30624608
[TBL] [Abstract][Full Text] [Related]
9. Optimality and identification of dynamic models in systems biology: an inverse optimal control framework.
Tsiantis N; Balsa-Canto E; Banga JR
Bioinformatics; 2018 Jul; 34(14):2433-2440. PubMed ID: 29522196
[TBL] [Abstract][Full Text] [Related]
10. MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.
Egea JA; Henriques D; Cokelaer T; Villaverde AF; MacNamara A; Danciu DP; Banga JR; Saez-Rodriguez J
BMC Bioinformatics; 2014 May; 15():136. PubMed ID: 24885957
[TBL] [Abstract][Full Text] [Related]
11. Improving dynamic predictions with ensembles of observable models.
Massonis G; Villaverde AF; Banga JR
Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36416122
[TBL] [Abstract][Full Text] [Related]
12. SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments.
Elsts A; Pentjuss A; Stalidzans E
Bioinformatics; 2017 Sep; 33(18):2966-2967. PubMed ID: 28679158
[TBL] [Abstract][Full Text] [Related]
13. MOJITOO: a fast and universal method for integration of multimodal single-cell data.
Cheng M; Li Z; Costa IG
Bioinformatics; 2022 Jun; 38(Suppl 1):i282-i289. PubMed ID: 35758807
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Global Optimization Approach for Parameter Estimation in Stochastic Dynamic Models of Biosystems.
Sequeiros C; Otero-Muras I; Vazquez C; Banga JR
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(3):1971-1982. PubMed ID: 36449576
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. OpenBioLink: a benchmarking framework for large-scale biomedical link prediction.
Breit A; Ott S; Agibetov A; Samwald M
Bioinformatics; 2020 Jul; 36(13):4097-4098. PubMed ID: 32339214
[TBL] [Abstract][Full Text] [Related]
18. Black box optimization benchmarking of the GLOBAL method.
Pál L; Csendes T; Markót MC; Neumaier A
Evol Comput; 2012; 20(4):609-39. PubMed ID: 22780907
[TBL] [Abstract][Full Text] [Related]
19. PESTO: Parameter EStimation TOolbox.
Stapor P; Weindl D; Ballnus B; Hug S; Loos C; Fiedler A; Krause S; Hroß S; Fröhlich F; Hasenauer J
Bioinformatics; 2018 Feb; 34(4):705-707. PubMed ID: 29069312
[TBL] [Abstract][Full Text] [Related]
20. Parameter estimation in biochemical pathways: a comparison of global optimization methods.
Moles CG; Mendes P; Banga JR
Genome Res; 2003 Nov; 13(11):2467-74. PubMed ID: 14559783
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
