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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

142 related articles for article (PubMed ID: 38717656)

  • 1. Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.
    Lam NN; Murray R; Docherty PD
    Bull Math Biol; 2024 May; 86(6):70. PubMed ID: 38717656
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.
    Flassig RJ; Migal I; der Zalm Ev; Rihko-Struckmann L; Sundmacher K
    BMC Bioinformatics; 2015 Jan; 16():13. PubMed ID: 25592474
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Making Predictions Using Poorly Identified Mathematical Models.
    Simpson MJ; Maclaren OJ
    Bull Math Biol; 2024 May; 86(7):80. PubMed ID: 38801489
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.
    Dhamala J; Arevalo HJ; Sapp J; Horácek BM; Wu KC; Trayanova NA; Wang L
    Med Image Anal; 2018 Aug; 48():43-57. PubMed ID: 29843078
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Accelerated maximum likelihood parameter estimation for stochastic biochemical systems.
    Daigle BJ; Roh MK; Petzold LR; Niemi J
    BMC Bioinformatics; 2012 May; 13():68. PubMed ID: 22548918
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling.
    Dotto CB; Mannina G; Kleidorfer M; Vezzaro L; Henrichs M; McCarthy DT; Freni G; Rauch W; Deletic A
    Water Res; 2012 May; 46(8):2545-58. PubMed ID: 22402270
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Local Riemannian geometry of model manifolds and its implications for practical parameter identifiability.
    Lill D; Timmer J; Kaschek D
    PLoS One; 2019; 14(6):e0217837. PubMed ID: 31158252
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Computational methods for a class of network models.
    Wang J; Jasra A; De Iorio M
    J Comput Biol; 2014 Feb; 21(2):141-61. PubMed ID: 24144112
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling.
    Dosne AG; Bergstrand M; Harling K; Karlsson MO
    J Pharmacokinet Pharmacodyn; 2016 Dec; 43(6):583-596. PubMed ID: 27730482
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Parametric and nonparametric population methods: their comparative performance in analysing a clinical dataset and two Monte Carlo simulation studies.
    Bustad A; Terziivanov D; Leary R; Port R; Schumitzky A; Jelliffe R
    Clin Pharmacokinet; 2006; 45(4):365-83. PubMed ID: 16584284
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Optimal experimental design with the sigma point method.
    Schenkendorf R; Kremling A; Mangold M
    IET Syst Biol; 2009 Jan; 3(1):10-23. PubMed ID: 19154081
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Calculating all multiple parameter solutions of ODE models to avoid biological misinterpretations.
    Saccomani MP; Thomaseth K
    Math Biosci Eng; 2019 Jul; 16(6):6438-6453. PubMed ID: 31698571
    [TBL] [Abstract][Full Text] [Related]  

  • 14. An efficient Monte Carlo method for estimating Ne from temporally spaced samples using a coalescent-based likelihood.
    Anderson EC
    Genetics; 2005 Jun; 170(2):955-67. PubMed ID: 15834143
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comparative assessment of parameter estimation methods in the presence of overdispersion: a simulation study.
    Roosa K; Luo R; Chowell G
    Math Biosci Eng; 2019 May; 16(5):4299-4313. PubMed ID: 31499663
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models.
    Roosa K; Chowell G
    Theor Biol Med Model; 2019 Jan; 16(1):1. PubMed ID: 30642334
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Bayesian calibration of process-based forest models: bridging the gap between models and data.
    Van Oijen M; Rougier J; Smith R
    Tree Physiol; 2005 Jul; 25(7):915-27. PubMed ID: 15870058
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A gradient Markov chain Monte Carlo algorithm for computing multivariate maximum likelihood estimates and posterior distributions: mixture dose-response assessment.
    Li R; Englehardt JD; Li X
    Risk Anal; 2012 Feb; 32(2):345-59. PubMed ID: 21906114
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Practical parameter identifiability for spatio-temporal models of cell invasion.
    Simpson MJ; Baker RE; Vittadello ST; Maclaren OJ
    J R Soc Interface; 2020 Mar; 17(164):20200055. PubMed ID: 32126193
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Parameter identification in dynamical models of anaerobic waste water treatment.
    Müller TG; Noykova N; Gyllenberg M; Timmer J
    Math Biosci; 2002; 177-178():147-60. PubMed ID: 11965253
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.