These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

88 related articles for article (PubMed ID: 16599737)

  • 1. Error criteria for cross validation in the context of chaotic time series prediction.
    Lim TP; Puthusserypady S
    Chaos; 2006 Mar; 16(1):013106. PubMed ID: 16599737
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Bias-correction of regression models: a case study on hERG inhibition.
    Hansen K; Rathke F; Schroeter T; Rast G; Fox T; Kriegl JM; Mika S
    J Chem Inf Model; 2009 Jun; 49(6):1486-96. PubMed ID: 19435326
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Sensitivity analysis of kappa-fold cross validation in prediction error estimation.
    Rodríguez JD; Pérez A; Lozano JA
    IEEE Trans Pattern Anal Mach Intell; 2010 Mar; 32(3):569-75. PubMed ID: 20075479
    [TBL] [Abstract][Full Text] [Related]  

  • 4. An Australian risk prediction model for 30-day mortality after isolated coronary artery bypass: the AusSCORE.
    Reid C; Billah B; Dinh D; Smith J; Skillington P; Yii M; Seevanayagam S; Mohajeri M; Shardey G
    J Thorac Cardiovasc Surg; 2009 Oct; 138(4):904-10. PubMed ID: 19660369
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Gene selection in cancer classification using sparse logistic regression with Bayesian regularization.
    Cawley GC; Talbot NL
    Bioinformatics; 2006 Oct; 22(19):2348-55. PubMed ID: 16844704
    [TBL] [Abstract][Full Text] [Related]  

  • 6. New models for old questions: generalized linear models for cost prediction.
    Moran JL; Solomon PJ; Peisach AR; Martin J
    J Eval Clin Pract; 2007 Jun; 13(3):381-9. PubMed ID: 17518803
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Using chaotic interrogation and attractor nonlinear cross-prediction error to detect fastener preload loss in an aluminum frame.
    Todd MD; Erickson K; Chang L; Lee K; Nichols JM
    Chaos; 2004 Jun; 14(2):387-99. PubMed ID: 15189067
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Error bounds for data-driven models of dynamical systems.
    Oleng' NO; Gribok A; Reifman J
    Comput Biol Med; 2007 May; 37(5):670-9. PubMed ID: 16895726
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Classification based upon gene expression data: bias and precision of error rates.
    Wood IA; Visscher PM; Mengersen KL
    Bioinformatics; 2007 Jun; 23(11):1363-70. PubMed ID: 17392326
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Unbiased reconstruction of the dynamics underlying a noisy chaotic time series.
    Jaeger L; Kantz H
    Chaos; 1996 Sep; 6(3):440-450. PubMed ID: 12780274
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. 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; 109(43):20565-71. PubMed ID: 16853662
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dose detection of radiated rice by infrared spectroscopy and chemometrics.
    Shao Y; He Y; Wu C
    J Agric Food Chem; 2008 Jun; 56(11):3960-5. PubMed ID: 18473474
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Estimating the predictive quality of dose-response after model selection.
    Hu C; Dong Y
    Stat Med; 2007 Jul; 26(16):3114-39. PubMed ID: 17206594
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Conceptual complexity and the bias/variance tradeoff.
    Briscoe E; Feldman J
    Cognition; 2011 Jan; 118(1):2-16. PubMed ID: 21112048
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Longitudinal variable selection by cross-validation in the case of many covariates.
    Cantoni E; Field C; Mills Flemming J; Ronchetti E
    Stat Med; 2007 Feb; 26(4):919-30. PubMed ID: 16625521
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins.
    Söllner J
    J Mol Recognit; 2006; 19(3):209-14. PubMed ID: 16602136
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Calculating confidence intervals for prediction error in microarray classification using resampling.
    Jiang W; Varma S; Simon R
    Stat Appl Genet Mol Biol; 2008; 7(1):Article8. PubMed ID: 18312213
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates.
    Lord D; Park PY
    Accid Anal Prev; 2008 Jul; 40(4):1441-57. PubMed ID: 18606278
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.