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 *

122 related articles for article (PubMed ID: 30273122)

  • 1. Reduced-Order Unscented Kalman Filter With Observations in the Frequency Domain: Application to Computational Hemodynamics.
    Muller LO; Caiazzo A; Blanco PJ
    IEEE Trans Biomed Eng; 2019 May; 66(5):1269-1276. PubMed ID: 30273122
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Assessment of reduced-order unscented Kalman filter for parameter identification in 1-dimensional blood flow models using experimental data.
    Caiazzo A; Caforio F; Montecinos G; Muller LO; Blanco PJ; Toro EF
    Int J Numer Method Biomed Eng; 2017 Aug; 33(8):e2843. PubMed ID: 27781397
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A methodological paradigm for patient-specific multi-scale CFD simulations: from clinical measurements to parameter estimates for individual analysis.
    Pant S; Fabrèges B; Gerbeau JF; Vignon-Clementel IE
    Int J Numer Method Biomed Eng; 2014 Dec; 30(12):1614-48. PubMed ID: 25345820
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Sequential parameter estimation for fluid-structure problems: application to hemodynamics.
    Bertoglio C; Moireau P; Gerbeau JF
    Int J Numer Method Biomed Eng; 2012 Apr; 28(4):434-55. PubMed ID: 25365657
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A flexible framework for sequential estimation of model parameters in computational hemodynamics.
    Arthurs CJ; Xiao N; Moireau P; Schaeffter T; Figueroa CA
    Adv Model Simul Eng Sci; 2020; 7(1):48. PubMed ID: 33282681
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Validation of Numerical Simulations of Thoracic Aorta Hemodynamics: Comparison with In Vivo Measurements and Stochastic Sensitivity Analysis.
    Boccadifuoco A; Mariotti A; Capellini K; Celi S; Salvetti MV
    Cardiovasc Eng Technol; 2018 Dec; 9(4):688-706. PubMed ID: 30357714
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Modeling of nonlinear biological phenomena modeled by S-systems.
    Mansouri MM; Nounou HN; Nounou MN; Datta AA
    Math Biosci; 2014 Mar; 249():75-91. PubMed ID: 24524881
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Data-Driven Identification of Stochastic Model Parameters and State Variables: Application to the Study of Cardiac Beat-to-Beat Variability.
    Sampedro-Puente DA; Fernandez-Bes J; Virag L; Varro A; Pueyo E
    IEEE J Biomed Health Inform; 2020 Mar; 24(3):693-704. PubMed ID: 31180875
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Parameter estimation of biological phenomena: an unscented Kalman filter approach.
    Meskin N; Nounou H; Nounou M; Datta A
    IEEE/ACM Trans Comput Biol Bioinform; 2013; 10(2):537-43. PubMed ID: 23929876
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Data assimilation for identification of cardiovascular network characteristics.
    Lal R; Mohammadi B; Nicoud F
    Int J Numer Method Biomed Eng; 2017 May; 33(5):. PubMed ID: 27531694
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A differentially private square root unscented Kalman filter for protecting process parameters in ICPSs.
    Yuan J; Wang Y; Ji Z
    ISA Trans; 2020 Sep; 104():44-52. PubMed ID: 31924313
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Physics-informed neural networks for parameter estimation in blood flow models.
    Garay J; Dunstan J; Uribe S; Sahli Costabal F
    Comput Biol Med; 2024 Aug; 178():108706. PubMed ID: 38879935
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A high-order local time stepping finite volume solver for one-dimensional blood flow simulations: application to the ADAN model.
    Müller LO; Blanco PJ; Watanabe SM; Feijóo RA
    Int J Numer Method Biomed Eng; 2016 Oct; 32(10):. PubMed ID: 26695621
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability.
    Pant S; Corsini C; Baker C; Hsia TY; Pennati G; Vignon-Clementel IE
    J R Soc Interface; 2017 Jan; 14(126):. PubMed ID: 28077762
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An improved constraint filtering technique for inferring hidden states and parameters of a biological model.
    Murtuza Baker S; Poskar CH; Schreiber F; Junker BH
    Bioinformatics; 2013 Apr; 29(8):1052-9. PubMed ID: 23434837
    [TBL] [Abstract][Full Text] [Related]  

  • 16. In vitro identification of four-element windkessel models based on iterated unscented Kalman filter.
    Huang H; Yang M; Zang W; Wu S; Pang Y
    IEEE Trans Biomed Eng; 2011 Sep; 58(9):2672-80. PubMed ID: 21859593
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Practical identifiability and uncertainty quantification of a pulsatile cardiovascular model.
    Marquis AD; Arnold A; Dean-Bernhoft C; Carlson BE; Olufsen MS
    Math Biosci; 2018 Oct; 304():9-24. PubMed ID: 30017910
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation.
    Liu X; Qu H; Zhao J; Yue P; Wang M
    Sensors (Basel); 2016 Sep; 16(9):. PubMed ID: 27657069
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Tumor growth modeling: Parameter estimation with Maximum Likelihood methods.
    Patmanidis S; Charalampidis AC; Kordonis I; Mitsis GD; Papavassilopoulos GP
    Comput Methods Programs Biomed; 2018 Jul; 160():1-10. PubMed ID: 29728236
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced-order unscented Kalman filter.
    Xi J; Lamata P; Lee J; Moireau P; Chapelle D; Smith N
    J Mech Behav Biomed Mater; 2011 Oct; 4(7):1090-102. PubMed ID: 21783118
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.