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Journal Abstract Search

108 related items for PubMed ID: 27510356

  • 21. Comparison of ridge regression, partial least-squares, pairwise correlation, forward- and best subset selection methods for prediction of retention indices for aliphatic alcohols.
    Farkas O, Héberger K.
    J Chem Inf Model; 2005; 45(2):339-46. PubMed ID: 15807497
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  • 22. QSPR Prediction of Lipophilicity for Organic Compounds Using Random Forest Technique on the Basis of Simplex Representation of Molecular Structure.
    Ognichenko LN, Kuz'min VE, Gorb L, Hill FC, Artemenko AG, Polischuk PG, Leszczynski J.
    Mol Inform; 2012 Apr; 31(3-4):273-80. PubMed ID: 27477097
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  • 23. 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
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  • 24. 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 13; 1102(1-2):238-44. PubMed ID: 16288769
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  • 25. Prediction of retention indices for identification of fatty acid methyl esters.
    Farkas O, Zenkevich IG, Stout F, Kalivas JH, Héberger K.
    J Chromatogr A; 2008 Jul 11; 1198-1199():188-95. PubMed ID: 18533170
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  • 30. QSPR prediction of GC retention indices for nitrogen-containing polycyclic aromatic compounds from heuristically computed molecular descriptors.
    Hu RJ, Liu HX, Zhang RS, Xue CX, Yao XJ, Liu MC, Hu ZD, Fan BT.
    Talanta; 2005 Nov 15; 68(1):31-9. PubMed ID: 18970281
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  • 33. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.
    Ventura C, Latino DA, Martins F.
    Eur J Med Chem; 2013 Nov 15; 70():831-45. PubMed ID: 24246731
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  • 34. Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores.
    Tetko IV, Solov'ev VP, Antonov AV, Yao X, Doucet JP, Fan B, Hoonakker F, Fourches D, Jost P, Lachiche N, Varnek A.
    J Chem Inf Model; 2006 Nov 15; 46(2):808-19. PubMed ID: 16563012
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  • 35. Predictions of chromatographic retention indices of alkylphenols with support vector machines and multiple linear regression.
    Fatemi MH, Baher E, Ghorbanzade'h M.
    J Sep Sci; 2009 Dec 15; 32(23-24):4133-42. PubMed ID: 19937857
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  • 38. Development of validated quantitative structure-retention relationship models for retention indices of plant essential oils.
    Qin LT, Liu SS, Chen F, Wu QS.
    J Sep Sci; 2013 May 15; 36(9-10):1553-60. PubMed ID: 23441046
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  • 39. Linear and nonlinear quantitative structure-property relationship models for solubility of some anthraquinone, anthrone and xanthone derivatives in supercritical carbon dioxide.
    Hemmateenejad B, Shamsipur M, Miri R, Elyasi M, Foroghinia F, Sharghi H.
    Anal Chim Acta; 2008 Mar 03; 610(1):25-34. PubMed ID: 18267136
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  • 40. 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 09; 1190(1-2):241-52. PubMed ID: 18395736
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