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 *

144 related articles for article (PubMed ID: 35153587)

  • 1. Bayesian uncertainty quantification for data-driven equation learning.
    Martina-Perez S; Simpson MJ; Baker RE
    Proc Math Phys Eng Sci; 2021 Oct; 477(2254):20210426. PubMed ID: 35153587
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Learning differential equation models from stochastic agent-based model simulations.
    Nardini JT; Baker RE; Simpson MJ; Flores KB
    J R Soc Interface; 2021 Mar; 18(176):20200987. PubMed ID: 33726540
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation.
    Albert C; Ulzega S; Stoop R
    Phys Rev E; 2016 Apr; 93():043313. PubMed ID: 27176434
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Learning Equations from Biological Data with Limited Time Samples.
    Nardini JT; Lagergren JH; Hawkins-Daarud A; Curtin L; Morris B; Rutter EM; Swanson KR; Flores KB
    Bull Math Biol; 2020 Sep; 82(9):119. PubMed ID: 32909137
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation.
    Cunha A; Barton DAW; Ritto TG
    Nonlinear Dyn; 2023; 111(10):9649-9679. PubMed ID: 37025428
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes.
    Fröhlich F; Loos C; Hasenauer J
    Methods Mol Biol; 2019; 1883():385-422. PubMed ID: 30547409
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Inference and uncertainty quantification of stochastic gene expression via synthetic models.
    Öcal K; Gutmann MU; Sanguinetti G; Grima R
    J R Soc Interface; 2022 Jul; 19(192):20220153. PubMed ID: 35858045
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Bayesian calibration, process modeling and uncertainty quantification in biotechnology.
    Helleckes LM; Osthege M; Wiechert W; von Lieres E; Oldiges M
    PLoS Comput Biol; 2022 Mar; 18(3):e1009223. PubMed ID: 35255090
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI.
    Tanno R; Worrall DE; Kaden E; Ghosh A; Grussu F; Bizzi A; Sotiropoulos SN; Criminisi A; Alexander DC
    Neuroimage; 2021 Jan; 225():117366. PubMed ID: 33039617
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Bayesian differential programming for robust systems identification under uncertainty.
    Yang Y; Aziz Bhouri M; Perdikaris P
    Proc Math Phys Eng Sci; 2020 Nov; 476(2243):20200290. PubMed ID: 33362409
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A Primer for parameter uncertainty, identifiability, and forecasts.
    Chowell G
    Infect Dis Model; 2017 Aug; 2(3):379-398. PubMed ID: 29250607
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Bayesian inference of constitutive model parameters from uncertain uniaxial experiments on murine tendons.
    Akintunde AR; Miller KS; Schiavazzi DE
    J Mech Behav Biomed Mater; 2019 Aug; 96():285-300. PubMed ID: 31078970
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Robust data-driven discovery of governing physical laws with error bars.
    Zhang S; Lin G
    Proc Math Phys Eng Sci; 2018 Sep; 474(2217):20180305. PubMed ID: 30333709
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Statistical analysis of differential equations: introducing probability measures on numerical solutions.
    Conrad PR; Girolami M; Särkkä S; Stuart A; Zygalakis K
    Stat Comput; 2017; 27(4):1065-1082. PubMed ID: 32226237
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Considering discrepancy when calibrating a mechanistic electrophysiology model.
    Lei CL; Ghosh S; Whittaker DG; Aboelkassem Y; Beattie KA; Cantwell CD; Delhaas T; Houston C; Novaes GM; Panfilov AV; Pathmanathan P; Riabiz M; Dos Santos RW; Walmsley J; Worden K; Mirams GR; Wilkinson RD
    Philos Trans A Math Phys Eng Sci; 2020 Jun; 378(2173):20190349. PubMed ID: 32448065
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation.
    Vo BN; Drovandi CC; Pettitt AN; Simpson MJ
    Math Biosci; 2015 May; 263():133-42. PubMed ID: 25747415
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Stochastic Control for Bayesian Neural Network Training.
    Winkler L; Ojeda C; Opper M
    Entropy (Basel); 2022 Aug; 24(8):. PubMed ID: 36010761
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models.
    Eriksson O; Jauhiainen A; Maad Sasane S; Kramer A; Nair AG; Sartorius C; Hellgren Kotaleski J
    Bioinformatics; 2019 Jan; 35(2):284-292. PubMed ID: 30010712
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.