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Journal Abstract Search

306 related items for PubMed ID: 19463555

  • 1. Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches.
    Tian F, Yang L, Lv F, Zhou P.
    Anal Chim Acta; 2009 Jun 30; 644(1-2):10-6. PubMed ID: 19463555
    [Abstract] [Full Text] [Related]

  • 2. Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome.
    Zhou P, Tian F, Lv F, Shang Z.
    J Chromatogr A; 2009 Apr 10; 1216(15):3107-16. PubMed ID: 19232620
    [Abstract] [Full Text] [Related]

  • 3. Modeling and prediction of peptide drift times in ion mobility spectrometry using sequence-based and structure-based approaches.
    Zhang Y, Jin Q, Wang S, Ren R.
    Comput Biol Med; 2011 May 10; 41(5):272-7. PubMed ID: 21439562
    [Abstract] [Full Text] [Related]

  • 4. Modeling and prediction of retention behavior of histidine-containing peptides in immobilized metal-affinity chromatography.
    Tian F, Yang L, Lv F, Zhou P.
    J Sep Sci; 2009 Jun 10; 32(12):2159-69. PubMed ID: 19548218
    [Abstract] [Full Text] [Related]

  • 5. Retention prediction of peptides based on uninformative variable elimination by partial least squares.
    Put R, Daszykowski M, Baczek T, Vander Heyden Y.
    J Proteome Res; 2006 Jul 10; 5(7):1618-25. PubMed ID: 16823969
    [Abstract] [Full Text] [Related]

  • 6. Novel approaches to predict the retention of histidine-containing peptides in immobilized metal-affinity chromatography.
    Du H, Zhang X, Wang J, Yao X, Hu Z.
    Proteomics; 2008 Jun 10; 8(11):2185-95. PubMed ID: 18446801
    [Abstract] [Full Text] [Related]

  • 7. The evaluation of two-step multivariate adaptive regression splines for chromatographic retention prediction of peptides.
    Put R, Vander Heyden Y.
    Proteomics; 2007 May 10; 7(10):1664-77. PubMed ID: 17443841
    [Abstract] [Full Text] [Related]

  • 8. Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine.
    Riahi S, Pourbasheer E, Ganjali MR, Norouzi P.
    J Hazard Mater; 2009 Jul 30; 166(2-3):853-9. PubMed ID: 19144466
    [Abstract] [Full Text] [Related]

  • 9. Advanced QSRR modeling of peptides behavior in RPLC.
    Bodzioch K, Durand A, Kaliszan R, Baczek T, Vander Heyden Y.
    Talanta; 2010 Jun 15; 81(4-5):1711-8. PubMed ID: 20441962
    [Abstract] [Full Text] [Related]

  • 10. Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine.
    Liu H, Yao X, Zhang R, Liu M, Hu Z, Fan B.
    J Phys Chem B; 2005 Nov 03; 109(43):20565-71. PubMed ID: 16853662
    [Abstract] [Full Text] [Related]

  • 11. Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine.
    Liu HX, Xue CX, Zhang RS, Yao XJ, Liu MC, Hu ZD, Fan BT.
    J Chem Inf Comput Sci; 2004 Nov 03; 44(6):1979-86. PubMed ID: 15554667
    [Abstract] [Full Text] [Related]

  • 12. Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives.
    Mandal AS, Roy K.
    Eur J Med Chem; 2009 Apr 03; 44(4):1509-24. PubMed ID: 18760864
    [Abstract] [Full Text] [Related]

  • 13. Comparative multiple quantitative structure-retention relationships modeling of gas chromatographic retention time of essential oils using multiple linear regression, principal component regression, and partial least squares techniques.
    Qin LT, Liu SS, Liu HL, Tong J.
    J Chromatogr A; 2009 Jul 03; 1216(27):5302-12. PubMed ID: 19486989
    [Abstract] [Full Text] [Related]

  • 14. 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 16; 1216(42):7055-62. PubMed ID: 19747683
    [Abstract] [Full Text] [Related]

  • 15. Prediction of gas chromatography/electron capture detector retention times of chlorinated pesticides, herbicides, and organohalides by multivariate chemometrics methods.
    Ghasemi J, Asadpour S, Abdolmaleki A.
    Anal Chim Acta; 2007 Apr 11; 588(2):200-6. PubMed ID: 17386811
    [Abstract] [Full Text] [Related]

  • 16. QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines.
    Yao X, Liu H, Zhang R, Liu M, Hu Z, Panaye A, Doucet JP, Fan B.
    Mol Pharm; 2005 Apr 11; 2(5):348-56. PubMed ID: 16196487
    [Abstract] [Full Text] [Related]

  • 17. Quantitative structure-retention relationship studies with immobilized artificial membrane chromatography II: partial least squares regression.
    Li J, Sun J, He Z.
    J Chromatogr A; 2007 Jan 26; 1140(1-2):174-9. PubMed ID: 17161410
    [Abstract] [Full Text] [Related]

  • 18. The molecular descriptor logSumAA and its alternatives in QSRR models to predict the retention of peptides.
    Bodzioch K, Baczek T, Kaliszan R, Vander Heyden Y.
    J Pharm Biomed Anal; 2009 Nov 01; 50(4):563-9. PubMed ID: 18929455
    [Abstract] [Full Text] [Related]

  • 19. Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure-retention relationships.
    Put R, Vander Heyden Y.
    Anal Chim Acta; 2007 Oct 29; 602(2):164-72. PubMed ID: 17933600
    [Abstract] [Full Text] [Related]

  • 20. Comparative evaluation of high-performance liquid chromatography stationary phases used for the separation of peptides in terms of quantitative structure-retention relationships.
    Michel M, Baczek T, Studzińska S, Bodzioch K, Jonsson T, Kaliszan R, Buszewski B.
    J Chromatogr A; 2007 Dec 14; 1175(1):49-54. PubMed ID: 17980378
    [Abstract] [Full Text] [Related]

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