The ties problem resulting from counting-based error estimators and its impact on gene selection algorithms

Bioinformatics

Volumn 22, Issue 20, 2006, Pages 2507-2515

  Zhou, Xin ^a,b   Mao, K Z ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE GRANULOCYTIC LEUKEMIA; ACUTE LYMPHOBLASTIC LEUKEMIA; ANALYTICAL ERROR; ARTICLE; BIOINFORMATICS; BREAST CANCER; CANCER GENETICS; COLON CANCER; CONTROLLED STUDY; GENETIC ALGORITHM; GENETIC SELECTION; HUMAN; INTERMETHOD COMPARISON; PRIORITY JOURNAL; UNCERTAINTY;

EID: 33749820670     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btl438     Document Type: Article

References (26)

1. Alon,U. et al. (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA, 96, 6745-6750.
2. Braga-Neto,U. and Dougherty,E.R. (2004a) Bolstered error estimation. Pattern Recog., 37, 1267-1281.
3. Braga-Neto,U. and Dougherty,E.R. (2004b) Is cross-validation valid for small-sample microarray classification? Bioinformatics, 20, 374-380.
4. Cho,J.-H. et al. (2004) Gene selection and classification from microarray data using kernel machine. FEBS Lett., 571, 93-98.
5. Dash,M. and Liu,H. (1997) Feature selection for classification. Intell. Data Anal., 1, 131-156.
6. Devijver,P. and Kittler,J. (1982) Pattern Recognition: A Statistical Approach. Prentice Hall, London.
7. Dudoit,S. et al. (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc., 97, 77-87.
8. Efron,B. and Tibshirani,R. (1997) Improvements on cross-validation: The .632+ bootstrap method. J. Am. Stat. Assoc., 92, 548-560.
9. Furey,T. et al. (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16, 906-914.
10. Golub,T. et al. (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
11. Guyon,I. et al. (2002) Gene selection for cancer classification using support vector machines. Mach. Learn., 46, 389-422.
12. Inza,I. et al. (2002) Gene selection by sequential search wrapper approaches in microarray cancer class prediction. J. Intell. Fuzzy Syst., 12, 25-33.
13. John,G.H., Kohavi,R. and Pfleger,K. (1994) Irrelevant features and the subset selection problem. In Proceedings of the Eleventh International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco, CA, pp. 121-129.
14. Kohavi,R. and John,G.H. (1997) Wrappers for feature subset selection. Artif. Intell., 97, 273-324.
15. Kononenko,I. (1994) Estimating attributes: Analysis and extensions of RELIEF. In Bergadano,F. and Raedt,L.D. (eds), ECML, vol. 784, of Lecture Notes in Computer Science. Springer, Berlin, pp. 171-182.
16. LeCun,Y., Denker,J. and Solla,S. (1990) Optimal brain damage. In Touretzky,D.S. (ed.), Advances in Neural Information Processing Systems II. Morgan Kaufman Publishers, San Mateo, CA, pp. 598-605.
17. Li,L. et al. (2001) Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics, 17, 1131-1142.
18. Luntz,A. and Brailovsky,V. (1969) On estimation of characters obtained in statistical procedure of recognition (in Russian). Technicheskaya Kibernatica, 3.
19. Ooi,C.H. and Tan,P. (2003) Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics, 19, 37-44.
20. Pudil,P. et al. (1994) Floating search methods in feature selection. Pattern Recogn. Lett., 15, 1119-1125.
21. Sima,C. et al. (2005a) Impact of error estimation on feature selection. Pattern Recogn., 38, 2472-2482.
22. Sima,C. et al. (2005b) Superior feature-set ranking for small samples using bolstered error estimation. Bioinformatics, 21, 1046-1054.
23. Vapnik,V.N. (1998) Statistical Learning Theory. John Wiley and Sons, New York.
24. West,M. et al. (2001) Predicting the clinical status of human breast cancer by using gene expression profiles. Proc. Natl Acad. Sci. USA, 98, 11462-11467.
25. Xiong,M. et al. (2001) Biomarker identification by feature wrappers. Genome Res., 11, 1878-1887.
26. Zhou,X. and Mao,K.Z. (2005) LS Bound based gene selection for DNA microarray data. Bioinformatics, 21, 1559-1564.