BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

120 related articles for article (PubMed ID: 38940161)

  • 1. Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.
    Dorešić D; Grein S; Hasenauer J
    Bioinformatics; 2024 Jun; 40(Supplement_1):i558-i566. PubMed ID: 38940161
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.
    Schälte Y; Fröhlich F; Jost PJ; Vanhoefer J; Pathirana D; Stapor P; Lakrisenko P; Wang D; Raimúndez E; Merkt S; Schmiester L; Städter P; Grein S; Dudkin E; Doresic D; Weindl D; Hasenauer J
    Bioinformatics; 2023 Nov; 39(11):. PubMed ID: 37995297
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Parameter estimation for dynamical systems with discrete events and logical operations.
    Fröhlich F; Theis FJ; Rädler JO; Hasenauer J
    Bioinformatics; 2017 Apr; 33(7):1049-1056. PubMed ID: 28040696
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach.
    Schmiester L; Weindl D; Hasenauer J
    J Math Biol; 2020 Aug; 81(2):603-623. PubMed ID: 32696085
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Hierarchical optimization for the efficient parametrization of ODE models.
    Loos C; Krause S; Hasenauer J
    Bioinformatics; 2018 Dec; 34(24):4266-4273. PubMed ID: 30010716
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. An easy and efficient approach for testing identifiability.
    Kreutz C
    Bioinformatics; 2018 Jun; 34(11):1913-1921. PubMed ID: 29365095
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A novel strategy for dynamic modeling of genome-scale interaction networks.
    Borzou P; Ghaisari J; Izadi I; Eshraghi Y; Gheisari Y
    Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36825834
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation.
    Schälte Y; Hasenauer J
    Bioinformatics; 2020 Jul; 36(Suppl_1):i551-i559. PubMed ID: 32657404
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Parameter estimation of dynamic biological network models using integrated fluxes.
    Liu Y; Gunawan R
    BMC Syst Biol; 2014 Nov; 8():127. PubMed ID: 25403239
    [TBL] [Abstract][Full Text] [Related]  

  • 13. PyDREAM: high-dimensional parameter inference for biological models in python.
    Shockley EM; Vrugt JA; Lopez CF
    Bioinformatics; 2018 Feb; 34(4):695-697. PubMed ID: 29028896
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics.
    Bortz DM; Messenger DA; Dukic V
    Bull Math Biol; 2023 Oct; 85(11):110. PubMed ID: 37796411
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Reliable and efficient parameter estimation using approximate continuum limit descriptions of stochastic models.
    Simpson MJ; Baker RE; Buenzli PR; Nicholson R; Maclaren OJ
    J Theor Biol; 2022 Sep; 549():111201. PubMed ID: 35752285
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks.
    Lakrisenko P; Stapor P; Grein S; Paszkowski Ł; Pathirana D; Fröhlich F; Lines GT; Weindl D; Hasenauer J
    PLoS Comput Biol; 2023 Jan; 19(1):e1010783. PubMed ID: 36595539
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI.
    Degasperi A; Nguyen LK; Fey D; Kholodenko BN
    Methods Mol Biol; 2022; 2385():91-115. PubMed ID: 34888717
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 6.