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 *

131 related articles for article (PubMed ID: 11204229)

  • 1. Artificial neural networks in liquid chromatography: efficient and improved quantitative structure-retention relationship models.
    Loukas YL
    J Chromatogr A; 2000 Dec; 904(2):119-29. PubMed ID: 11204229
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Quantitative structure-binding relationships (QSBR) and artificial neural networks: improved predictions in drug:cyclodextrin inclusion complexes.
    Loukas YL
    Int J Pharm; 2001 Sep; 226(1-2):207-11. PubMed ID: 11532583
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography.
    Krmar J; Vukićević M; Kovačević A; Protić A; Zečević M; Otašević B
    J Chromatogr A; 2020 Jul; 1623():461146. PubMed ID: 32505269
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure-retention relationships.
    Put R; Vander Heyden Y
    Anal Chim Acta; 2007 Oct; 602(2):164-72. PubMed ID: 17933600
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases.
    Biancolillo A; D'Archivio AA
    J Chromatogr A; 2022 Jan; 1663():462758. PubMed ID: 34954535
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures.
    Taraji M; Haddad PR; Amos RI; Talebi M; Szucs R; Dolan JW; Pohl CA
    J Chromatogr A; 2017 Feb; 1486():59-67. PubMed ID: 28049585
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model.
    Park SH; Haddad PR; Talebi M; Tyteca E; Amos RI; Szucs R; Dolan JW; Pohl CA
    J Chromatogr A; 2017 Feb; 1486():68-75. PubMed ID: 28057331
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches.
    Žuvela P; Macur K; Jay Liu J; Bączek T
    J Pharm Biomed Anal; 2016 Aug; 127():94-100. PubMed ID: 26856456
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Structure-response relationship in electrospray ionization-mass spectrometry of sartans by artificial neural networks.
    Golubović J; Birkemeyer C; Protić A; Otašević B; Zečević M
    J Chromatogr A; 2016 Mar; 1438():123-32. PubMed ID: 26884139
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography.
    Park SH; Talebi M; Amos RIJ; Tyteca E; Haddad PR; Szucs R; Pohl CA; Dolan JW
    J Chromatogr A; 2017 Nov; 1523():173-182. PubMed ID: 28291517
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Combination of artificial neural network technique and linear free energy relationship parameters in the prediction of gradient retention times in liquid chromatography.
    Fatemi MH; Abraham MH; Poole CF
    J Chromatogr A; 2008 May; 1190(1-2):241-52. PubMed ID: 18395736
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Application of artificial neural network to predict the retention time of drug metabolites in two-dimensional liquid chromatography.
    Noorizadeh H; Sobhan-Ardakani S; Raoofi F; Noorizadeh M; Mortazavi SS; Ahmadi T; Pournajafi K
    Drug Test Anal; 2013 May; 5(5):315-9. PubMed ID: 22012704
    [TBL] [Abstract][Full Text] [Related]  

  • 13. New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists.
    Oliveira AA; Lipinski CF; Pereira EB; Honorio KM; Oliveira PR; Weber KC; Romero RAF; de Sousa AG; da Silva ABF
    J Mol Model; 2017 Oct; 23(10):302. PubMed ID: 28971260
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid, uric acid, dopamine and nitrite: application of non-bilinear voltammetric data for exploiting first-order advantage.
    Gholivand MB; Jalalvand AR; Goicoechea HC; Skov T
    Talanta; 2014 Feb; 119():553-63. PubMed ID: 24401455
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Application of artificial neural networks for prediction of retention factors of triazine herbicides in reversed-phase liquid chromatography.
    Ruggieri F; D'Archivio AA; Carlucci G; Mazzeo P
    J Chromatogr A; 2005 May; 1076(1-2):163-9. PubMed ID: 15974083
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Cross-column prediction of gas-chromatographic retention of polychlorinated biphenyls by artificial neural networks.
    D'Archivio AA; Incani A; Ruggieri F
    J Chromatogr A; 2011 Dec; 1218(48):8679-90. PubMed ID: 22000780
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks.
    Gupta VK; Khani H; Ahmadi-Roudi B; Mirakhorli S; Fereyduni E; Agarwal S
    Talanta; 2011 Jan; 83(3):1014-22. PubMed ID: 21147352
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Performance comparison of partial least squares-related variable selection methods for quantitative structure retention relationships modelling of retention times in reversed-phase liquid chromatography.
    Talebi M; Schuster G; Shellie RA; Szucs R; Haddad PR
    J Chromatogr A; 2015 Dec; 1424():69-76. PubMed ID: 26592563
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of polar surface area of drug molecules: a QSPR approach.
    Noorizadeh H; Farmany A; Noorizadeh M; Kohzadi M
    Drug Test Anal; 2013 Apr; 5(4):222-7. PubMed ID: 21539000
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms.
    Bolanca T; Cerjan-Stefanović S; Regelja M; Regelja H; Loncarić S
    J Chromatogr A; 2005 Aug; 1085(1):74-85. PubMed ID: 16106851
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.