BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

194 related articles for article (PubMed ID: 20851045)

  • 1. An ensemble method based on uninformative variable elimination and mutual information for spectral multivariate calibration.
    Tan C; Wang J; Wu T; Qin X; Li M
    Spectrochim Acta A Mol Biomol Spectrosc; 2010 Dec; 77(5):960-4. PubMed ID: 20851045
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Improvement of spectral calibration for food analysis through multi-model fusion.
    Tan C; Chen H; Xu Z; Wu T; Wang L; Zhu W
    Spectrochim Acta A Mol Biomol Spectrosc; 2012 Oct; 96():526-31. PubMed ID: 22738883
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Variable-weighted least-squares support vector machine for multivariate spectral analysis.
    Zou HY; Wu HL; Fu HY; Tang LJ; Xu L; Nie JF; Yu RQ
    Talanta; 2010 Mar; 80(5):1698-701. PubMed ID: 20152399
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Mutual information-induced interval selection combined with kernel partial least squares for near-infrared spectral calibration.
    Tan C; Li M
    Spectrochim Acta A Mol Biomol Spectrosc; 2008 Dec; 71(4):1266-73. PubMed ID: 18462989
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration.
    Li H; Liang Y; Xu Q; Cao D
    Anal Chim Acta; 2009 Aug; 648(1):77-84. PubMed ID: 19616692
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.
    Andries JP; Vander Heyden Y; Buydens LM
    Anal Chim Acta; 2011 Oct; 705(1-2):292-305. PubMed ID: 21962372
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry.
    Tan C; Chen H; Wang C; Zhu W; Wu T; Diao Y
    Spectrochim Acta A Mol Biomol Spectrosc; 2013 Mar; 105():1-7. PubMed ID: 23274502
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An ensemble of Monte Carlo uninformative variable elimination for wavelength selection.
    Han QJ; Wu HL; Cai CB; Xu L; Yu RQ
    Anal Chim Acta; 2008 Apr; 612(2):121-5. PubMed ID: 18358856
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton-viscose textiles.
    Durand A; Devos O; Ruckebusch C; Huvenne JP
    Anal Chim Acta; 2007 Jul; 595(1-2):72-9. PubMed ID: 17605985
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice.
    Wu D; He Y; Nie P; Cao F; Bao Y
    Anal Chim Acta; 2010 Feb; 659(1-2):229-37. PubMed ID: 20103129
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Variables selection methods in near-infrared spectroscopy.
    Xiaobo Z; Jiewen Z; Povey MJ; Holmes M; Hanpin M
    Anal Chim Acta; 2010 May; 667(1-2):14-32. PubMed ID: 20441862
    [TBL] [Abstract][Full Text] [Related]  

  • 12. New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in illicit street drugs.
    Moros J; Kuligowski J; Quintás G; Garrigues S; de la Guardia M
    Anal Chim Acta; 2008 Dec; 630(2):150-60. PubMed ID: 19012826
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Effects of nonlinearities and uncorrelated or correlated errors in realistic simulated data on the prediction abilities of augmented classical least squares and partial least squares.
    Melgaard DK; Haaland DM
    Appl Spectrosc; 2004 Sep; 58(9):1065-73. PubMed ID: 15479523
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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; 5(7):1618-25. PubMed ID: 16823969
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Determination of alpha-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination.
    Wu D; Chen X; Shi P; Wang S; Feng F; He Y
    Anal Chim Acta; 2009 Feb; 634(2):166-71. PubMed ID: 19185115
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.
    Balabin RM; Smirnov SV
    Anal Chim Acta; 2011 Apr; 692(1-2):63-72. PubMed ID: 21501713
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration.
    Allegrini F; Olivieri AC
    Talanta; 2013 Oct; 115():755-60. PubMed ID: 24054659
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Parallel calibration revisited: the second direction for shrinkage estimation of regression coefficients can be as natural and necessary as the traditional one.
    Xu L; Yu XP; Lu XL; Wu YH; Wu HL; Jiang JH; Shen GL; Yu RQ
    Anal Chim Acta; 2009 Jun; 644(1-2):25-9. PubMed ID: 19463557
    [TBL] [Abstract][Full Text] [Related]  

  • 19. [Research on the robustness improvement of calibration model for measuring the contents of components in milk by multidimensional calibration in near-infrared spectroscopy].
    Peng D; Xu KX; Song Y
    Guang Pu Xue Yu Guang Pu Fen Xi; 2009 Apr; 29(4):913-7. PubMed ID: 19626871
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.
    Yun YH; Wang WT; Tan ML; Liang YZ; Li HD; Cao DS; Lu HM; Xu QS
    Anal Chim Acta; 2014 Jan; 807():36-43. PubMed ID: 24356218
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.