Model population analysis for variable selection

Journal of Chemometrics

Volumn 24, Issue 7-8, 2010, Pages 418-423

  Li, Hong Dong ^a   Liang, Yi Zeng ^a   Xu, Qing Song ^a   Cao, Dong Sheng ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Biomarker discovery; Model population analysis; Monte Carlo sampling; Variable selection

Indexed keywords

POPULATION STATISTICS;

BIO-MARKER DISCOVERY; BIOLOGICAL DATA; CLINICAL DATA; CONCEPT MODEL; MODEL POPULATION ANALYSE; MONTE CARLO SAMPLING; PARAMETER REGRESSIONS; POPULATION ANALYSIS; SUBMODELS; VARIABLES SELECTIONS;

MONTE CARLO METHODS;

EID: 77957041205     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1300     Document Type: Article

References (31)

1. Hawkins DM, Basak SC,Mills D. Assessing model fit by cross-validation. J. Chem. Inf. Comput. Sci. 2003; 43: 579-586.
2. Barker M, Rayens W. Partial least squares for discrimination. J. Chemom. 2003; 17: 166-173.
3. Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. 2001; 58: 109-130.
4. De Jong S. SIMPLS: An alternative approach to partial least squares regression. Chemom. Intell. Lab. 1993; 18: 251-263.
5. Yi L-Z, He J, Liang Y-Z, Yuan D-L, Chau F-T. Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA. FEBS Lett. 2006; 580: 6837-6845.
6. Cronin MTD, Schultz TW. Pitfalls in QSAR. J. Mol. Struct: Theochem 2003; 622: 39-51.
7. Golbraikh A, Tropsha A. Beware of q2! J. Mol. Graph. Model. 2002; 20: 269-276.
8. Tyers M, Mann M. From genomics to proteomics. Nature 2003; 422: 193-197.
9. Teramoto R, Aoki M, Kimura T, Kanaoka M. Prediction of siRNA functionality using generalized string kernel and support vector machine. FEBS Lett. 2005; 579: 2878-2882.
10. Lavine BK, Davidson CE, Rayens WS. Machine learning based pattern recognition applied to microarray data. Comb. Chem. High Scr. 2004; 7: 115-131.
11. Domon B, Aebersold R. Review-Mass spectrometry and protein analysis. Science 2006; 312: 212-217.
12. Nesvizhskii AI, Vitek O, Aebersold R. Aebersold, Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 2007; 4: 787-797.
13. Gavaghan CL, Holmes E, Lenz E, Wilson ID, Nicholson JK. An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:ApfCD mouse. FEBS Lett. 2000; 484: 169-174.
14. Kamleh MA, Hobani Y, Dow JAT, Watson DG. Metabolomic profiling of Drosophila using liquid chromatography Fourier transform mass spectrometry. FEBS Lett. 2008; 582: 2916-2922.
15. Rajalahti T, Arneberg R, Berven FS, Myhr K-M, Ulvik RJ, Kvalheim OM. Biomarker discovery in mass spectral profiles by means of selectivity ratio plot. Chemom. Intell. Lab. 2009; 95: 35-48.
16. Liu QZ, Sung AH, Qiao MY, Chen ZX, Yang JY, Yang MQ, Huang XD, Deng YP. Comparison of feature selection and classification for MALDI-MS data. BMC Genomics 2009; 10.
17. Niwa T. Biomarker discovery for kidney diseases by mass spectrometry. J. Chromatogr. B 2008; 870: 148-153.
18. Oh JH, Lotan Y, Gurnani P, Rosenblatt KP, Gao J. Prostate cancer biomarker discovery using high performance mass spectral serum profiling. Comput. Meth. Prog. Bio. 2009; 96: 33-41.
19. Abdel-Aal RE. GMDH-based feature ranking and selection for improved classification of medical data. J. Biomed. Inform. 2005; 38: 456-468.
20. Huang Y, McCullagh P, Black N, Harper R. Feature selection and classification model construction on type 2 diabetic patients' data. Artif. Intell. Med. 2007; 41: 251-262.
21. Barnes M, Freudenberg J, Thompson S, Aronow B, Pavlidis P. Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms. Nucleic Acids Res. 2005; 33: 5914-5923.
22. Tibshirani R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 1996; 58: 267-288.
23. Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. Ann. Stat. 2004; 32: 407-499.
24. Zou H, Hastie T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 2005; 67: 301-320.
25. Centner V, Massart D-L, de Noord OE, de Jong S, Vandeginste BM, Sterna C. Elimination of Uninformative Variables for Multivariate Calibration. Anal. Chem. 1996; 68: 3851-3858.
26. Cai W, Li Y, Shao X. A variable selection method based on uninformative variable elimination for multivariate calibration of nearinfrared spectra. Chemom. Intell. Lab. 2008; 90: 188-194.
27. Xu H, Liu Z, Cai W, Shao X. A wavelength selection method based on randomization test for near-infrared spectral analysis. Chemom. Intell. Lab. 2009; 97: 189-193.
28. Cao D-S, Liang Y-Z, Xu Q-S, Li H-D, Chen X. A new strategy of outlier detection for QSAR/QSPR. J. Comput. Chem. 2010; 31: 592-602.
29. Alexandrov T, Decker J, Mertens B, Deelder AM, Tollenaar RAEM, Maass P, Thiele H. Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation. Bioinformatics 2009; 25: 643-649.
30. Xu Q-S, Liang Y-Z. Monte Carlo cross validation. Chemometr. Chemom. Intell. Lab. 2001; 56: 1-11.
31. Li H-D, Liang Y-Z, Xu Q-S, Cao D-S. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal. Chim. Acta 2009; 648: 77-84.