BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

338 related articles for article (PubMed ID: 18395736)

  • 1. 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]  

  • 2. Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach.
    Jalali-Heravi M; Kyani A
    J Chem Inf Comput Sci; 2004; 44(4):1328-35. PubMed ID: 15272841
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography.
    Quiming NS; Denola NL; Saito Y; Jinno K
    J Sep Sci; 2008 May; 31(9):1550-63. PubMed ID: 18435511
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Development of retention prediction models for adrenoreceptor agonists and antagonists on a polyvinyl alcohol-bonded stationary phase in hydrophilic interaction chromatography.
    Quiming NS; Denola NL; Samsuri SR; Saito Y; Jinno K
    J Sep Sci; 2008 May; 31(9):1537-49. PubMed ID: 18428191
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of immobilized artificial membrane-liquid chromatography retention of some drugs from their molecular structure descriptors and LFER parameters.
    Fatemi MH; Shamseddin H
    J Sep Sci; 2009 Oct; 32(20):3395-402. PubMed ID: 19750506
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Quantitative structure migration relationship modeling of migration factor for some benzene derivatives in micellar electrokinetic chromatography.
    Fatemi MH; Shamseddin H; Malekzadeh H
    J Sep Sci; 2009 Jun; 32(11):1934-40. PubMed ID: 19425021
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes.
    Arulsudar N; Subramanian N; Muthy RS
    J Pharm Pharm Sci; 2005 Aug; 8(2):243-58. PubMed ID: 16124936
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.
    Albaugh DR; Hall LM; Hill DW; Kertesz TM; Parham M; Hall LH; Grant DF
    J Chem Inf Model; 2009 Apr; 49(4):788-99. PubMed ID: 19309176
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase.
    Fatemi MH; Malekzadeh H; Shamseddin H
    J Sep Sci; 2009 Feb; 32(4):653-9. PubMed ID: 19160374
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography.
    Golmohammadi H; Fatemi MH
    Electrophoresis; 2005 Sep; 26(18):3438-44. PubMed ID: 16110463
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Prediction of human skin permeability using artificial neural network (ANN) modeling.
    Chen LJ; Lian GP; Han LJ
    Acta Pharmacol Sin; 2007 Apr; 28(4):591-600. PubMed ID: 17376301
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multi-variable retention modelling in reversed-phase high-performance liquid chromatography based on the solvation method: a comparison between curvilinear and artificial neural network regression.
    D'Archivio AA; Maggi MA; Ruggieri F
    Anal Chim Acta; 2011 Mar; 690(1):35-46. PubMed ID: 21414434
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Quantitative study of the structure-retention index relationship in the imine family.
    Acevedo-Martínez J; Escalona-Arranz JC; Villar-Rojas A; Téllez-Palmero F; Pérez-Rosés R; González L; Carrasco-Velar R
    J Chromatogr A; 2006 Jan; 1102(1-2):238-44. PubMed ID: 16288769
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Quantitative structure-retention relationship studies for taxanes including epimers and isomeric metabolites in ultra fast liquid chromatography.
    Dong PP; Ge GB; Zhang YY; Ai CZ; Li GH; Zhu LL; Luan HW; Liu XB; Yang L
    J Chromatogr A; 2009 Oct; 1216(42):7055-62. PubMed ID: 19747683
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Modeling GC-ECD retention times of pentafluorobenzyl derivatives of phenol by using artificial neural networks.
    Asadpour-Zeynali K; Jalili-Jahani N
    J Sep Sci; 2008 Dec; 31(21):3788-95. PubMed ID: 18956382
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.
    Golmohammadi H
    J Comput Chem; 2009 Nov; 30(15):2455-65. PubMed ID: 19360793
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide.
    Tabaraki R; Khayamian T; Ensafi AA
    J Mol Graph Model; 2006 Sep; 25(1):46-54. PubMed ID: 16337156
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Retention prediction of adrenoreceptor agonists and antagonists on a diol column in hydrophilic interaction chromatography.
    Quiming NS; Denola NL; Ueta I; Saito Y; Tatematsu S; Jinno K
    Anal Chim Acta; 2007 Aug; 598(1):41-50. PubMed ID: 17693305
    [TBL] [Abstract][Full Text] [Related]  

  • 20. QSPR modeling of soil sorption coefficients (K(OC)) of pesticides using SPA-ANN and SPA-MLR.
    Goudarzi N; Goodarzi M; Araujo MC; Galvão RK
    J Agric Food Chem; 2009 Aug; 57(15):7153-8. PubMed ID: 19722589
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 17.