BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

226 related articles for article (PubMed ID: 17381081)

  • 1. Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points?
    Varnek A; Kireeva N; Tetko IV; Baskin II; Solov'ev VP
    J Chem Inf Model; 2007; 47(3):1111-22. PubMed ID: 17381081
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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; 46(2):808-19. PubMed ID: 16563012
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization.
    Nigsch F; Bender A; van Buuren B; Tissen J; Nigsch E; Mitchell JB
    J Chem Inf Model; 2006; 46(6):2412-22. PubMed ID: 17125183
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. General melting point prediction based on a diverse compound data set and artificial neural networks.
    Karthikeyan M; Glen RC; Bender A
    J Chem Inf Model; 2005; 45(3):581-90. PubMed ID: 15921448
    [TBL] [Abstract][Full Text] [Related]  

  • 6. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.
    Votano JR; Parham M; Hall LM; Hall LH; Kier LB; Oloff S; Tropsha A
    J Med Chem; 2006 Nov; 49(24):7169-81. PubMed ID: 17125269
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity.
    Xue CX; Zhang RS; Liu HX; Liu MC; Hu ZD; Fan BT
    J Chem Inf Comput Sci; 2004; 44(4):1267-74. PubMed ID: 15272834
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Highly correlating distance/connectivity-based topological indices 5. Accurate prediction of liquid density of organic molecules using PCR and PC-ANN.
    Shamsipur M; Ghavami R; Sharghi H; Hemmateenejad B
    J Mol Graph Model; 2008 Nov; 27(4):506-11. PubMed ID: 18948045
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Scores of extended connectivity fingerprint as descriptors in QSPR study of melting point and aqueous solubility.
    Zhou D; Alelyunas Y; Liu R
    J Chem Inf Model; 2008 May; 48(5):981-7. PubMed ID: 18465850
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Atom-type-based AI topological descriptors: application in structure-boiling point correlations of oxo organic compounds.
    Ren B
    J Chem Inf Comput Sci; 2003; 43(4):1121-31. PubMed ID: 12870902
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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; 32(23-24):4133-42. PubMed ID: 19937857
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Combinatorial QSAR modeling of P-glycoprotein substrates.
    de Cerqueira Lima P; Golbraikh A; Oloff S; Xiao Y; Tropsha A
    J Chem Inf Model; 2006; 46(3):1245-54. PubMed ID: 16711744
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression.
    Chen HF
    Anal Chim Acta; 2008 Feb; 609(1):24-36. PubMed ID: 18243870
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?
    Balabin RM; Lomakina EI
    Phys Chem Chem Phys; 2011 Jun; 13(24):11710-8. PubMed ID: 21594265
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. QSPR model of Henry's law constant for a diverse set of organic chemicals based on genetic algorithm-radial basis function network approach.
    Modarresi H; Modarress H; Dearden JC
    Chemosphere; 2007 Feb; 66(11):2067-76. PubMed ID: 17113627
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Toward generating simpler QSAR models: nonlinear multivariate regression versus several neural network ensembles and some related methods.
    Lucić B; Nadramija D; Basic I; Trinajstić N
    J Chem Inf Comput Sci; 2003; 43(4):1094-102. PubMed ID: 12870898
    [TBL] [Abstract][Full Text] [Related]  

  • 18. QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids.
    Oprisiu I; Varlamova E; Muratov E; Artemenko A; Marcou G; Polishchuk P; Kuz'min V; Varnek A
    Mol Inform; 2012 Jul; 31(6-7):491-502. PubMed ID: 27477467
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of ozone tropospheric degradation rate constants by projection pursuit regression.
    Ren Y; Liu H; Yao X; Liu M
    Anal Chim Acta; 2007 Apr; 589(1):150-8. PubMed ID: 17397666
    [TBL] [Abstract][Full Text] [Related]  

  • 20. QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses.
    Xu J; Wang L; Wang L; Shen X; Xu W
    J Comput Chem; 2011 Nov; 32(15):3241-52. PubMed ID: 21837634
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.