High-dimensional bolstered error estimation

Bioinformatics

Volumn 27, Issue 21, 2011, Pages 3056-3064

  Sima, Chao ^a   Braga Neto, Ulisses M ^b   Dougherty, Edward R ^a,b,c  

^a TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

^b Texas AandM University   (United States)

^c UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BREAST TUMOR; DNA MICROARRAY; FEMALE; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METABOLISM; MULTIPLE MYELOMA; REPRODUCIBILITY; SAMPLE SIZE; STATISTICAL ANALYSIS;

ALGORITHMS; BREAST NEOPLASMS; DATA INTERPRETATION, STATISTICAL; FEMALE; GENE EXPRESSION PROFILING; HUMANS; MULTIPLE MYELOMA; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS; SAMPLE SIZE;

EID: 80054915845     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr518     Document Type: Article

References (31)

1. Boulesteix,A.-L. (2010) Over-optimism in bioinformatics research. Bioinformatics, 26, 437-439.
2. Boulesteix,A.-L. and Strobl,C. (2009) Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction. BMC Med. Rese. Methodol., 9, 85.
3. Braga-Neto,U. (2007) Fads and fallacies in the name of small-sample microarray classification. IEEE Sig. Proc. Mag., 24, 91-99.
4. Braga-Neto,U. and Dougherty,E. (2004a) Bolstered error estimation. Pattern Recognit., 37, 1267-1281.
5. Braga-Neto,U.M. and Dougherty,E.R. (2004b) Is cross-validation valid for smallsample microarray classification? Bioinformatics, 20, 374-380.
6. Devroye,L. et al. (1996) A Probabilistic Theory of Pattern Recognition. Springer, New York.
7. Efron,B. (1983) Estimating the error rate of a prediction rule: Improvement on crossvalidation. J. Am. Stat. Assoc., 78, 316-331.
8. Elderton,S.W. and Johnson,N. (1969) Systems of Frequency Curves. Cambridge University Press, Cambridge.
9. Grate,L. (2005) Many accurate small-discriminatory feature subsets exist in microarray transcript data: biomarker discovery. BMC Bioinformatics, 6, 97.
10. Hanczar,B. et al. (2007) Decorrelation of the true and estimated classifier errors in high-dimensional settings. EURASIP J. Bioinformatics Syst. Biol., 2007, 1-12.
11. Hanczar,B. et al. (2010) Small-sample precision of roc-related estimates. Bioinformatics, 26, 822-830.
12. Hua,J. et al. (2005) Optimal number of features as a function of sample size for various classification rules. Bioinformatics, 21, 1509-1515.
13. Hua,J. et al. (2009) Performance of feature-selection methods in the classification of high-dimension data. Pattern Recognit., 42, 409-424.
14. Huynh,K. et al. (2007) Improved bolstering error estimation for gene ranking. In Proceedings of the IEEE EMBS. Lyon, France.
15. Jain,A. and Zongker,D. (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell., 19, 153-158.
16. Jelizarow,M. et al. (2010) Over-optimism in bioinformatics: an illustration. Bioinformatics, 26, 1990-1998.
17. Jiang,W. and Simon,R. (2007) A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification. Stat. Med., 26, 5320-5334.
18. Kudo,M. and Sklansky,J. (2000) Comparison of algorithms that select features for pattern classifiers. Pattern Recognit., 33, 25-41.
19. Molinaro,A.M. et al. (2005) Prediction error estimation: a comparison of resampling methods. Bioinformatics, 21, 3301-3307.
20. Pudil,P. et al. (1994) Floating search methods in feature-selection. Pattern Recognit. Lett., 15, 1119-1125.
21. Rocke,D.M. et al. (2009) Papers on normalization, variable selection, classification or clustering of microarray data. Bioinformatics, 25, 701-702.
22. Sima,C. and Dougherty,E. (2006a) What should be expected from feature selection in small-sample settings. Bioinformatics, 22, 2430-2436.
23. Sima,C. and Dougherty,E.R. (2006b) Optimal convex error estimators for classification. Pattern Recognit., 39, 1763-1780.
24. Sima,C. et al. (2005a) Impact of error estimation on feature selection. Pattern Recognit., 38, 2472-2482.
25. Sima,C. et al. (2005b) Superior feature-set ranking for small samples using bolstered error estimation. Bioinformatics, 21, 1046-1054.
26. van de Vijver,M.J. et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med., 347, 1999-2009.
27. van't Veer,L.J. et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530-536.
28. Vu,T. and Braga-Neto,U. (2008) Preliminary study on bolstered error estimation in high-dimensional spaces. In Proceedings of the IEEE GENSIPS. Phoenix, AZ.
29. Yousefi,M.R. et al. (2010) Reporting bias when using real data sets to analyze classification performance. Bioinformatics, 26, 68-76.
30. Zhan,F. et al. (2006) The molecular classification of multiple myeloma. Blood, 108, 2020-2028.
31. Zhao,C. et al. (2010) Characterization of the effectiveness of reporting lists of small feature sets relative to the accuracy of the prior biological knowledge. Cancer Inform., 9, 49-60.