On the comparison of classifiers for microarray data

Current Bioinformatics

Volumn 5, Issue 1, 2010, Pages 29-39

  Hanczar, Blaise ^a   Dougherty, Edward R ^b,c  

^a Huawei Technologies Co   (France)

^b Texas AandM University   (United States)

^c TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

Author keywords

Classifier comparison; Error estimation; Microarray classification; Variance study

Indexed keywords

ANALYSIS OF VARIANCE; ANALYTICAL ERROR; BINOMIAL DISTRIBUTION; BIOINFORMATICS; GENE REGULATORY NETWORK; MATHEMATICAL COMPUTING; MICROARRAY ANALYSIS; PRIORITY JOURNAL; REVIEW;

EID: 77950171443     PISSN: 15748936     EISSN: None     Source Type: Journal    
DOI: 10.2174/157489310790596376     Document Type: Review

References (26)

1. West M, Blanchette C, Dressman E, Huang H, Ishisa S, Spang R. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci USA 2001; 98: 11462-67.
2. Van de Vijver MJ, He YD, Van't Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. New Eng J Med 2002; 347: 1999-2009.
3. Halvorsen OJ. Gene expression profiles in prostate cancer: association with patient subgroups and tumour di® erentiation. Int J Oncol 2005; 26: 329-36.
4. Dudoit S, Fridlyand J, Speed P. Comparison of discrimination methods for classification of tumors using gene expression data. J Am Stat Assoc 2002; 97: 77-87.
5. Khan J, Wei Jun SJ, Ringnér M, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Natl Med 2001; 7: 673-79.
6. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 2005; 21: 631-43.
7. Diaz-Uriarte R, Alvarez de Andres S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 2006; 7: 3.
8. Isaksson A, Wallman M, GÄoransson H, Gustafsson MG. Crossvalidation and bootstrapping are unreliable in small sample classification. Pattern Recognit Lett 2008; 29: 1960-1965.
9. Braga-Neto UM, Dougherty ER. Is cross-validation valid for smallsample microarray classification. Bioinformatics 2004; 20: 374-80.
10. Wood I, Visscher P, Mengersen K. Classification based upon gene expression data: bias and precision of error rates. Bioinformatics 2007; 11: 1363-70.
11. Hanczar B, Hua J, Dougherty ER. Decorrelation of the true and estimated classifier errors in high-dimensional settings. EURASIP J Bioinform Systs Biol 2007; 38473.
12. Boulesteix AL, Strobl C, Augustin T, Daumer M. An extensive comparison of recent classification tools applied to microarray data. Cancer Inform 2008; 6: 77-97.
13. Handl J, Knowles J, Kell DB. Computational cluster validation in post-genomic data analysis. Bioinformatics 2005; 21: 3201-3212.
14. D'haeseleer P, Liang S, Somogyi R. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 2000; 16: 707-26.
15. Friedman N, Linial M, Nachman I, Pe'er D. Using bayesian networks to analyze expression data. J Comp Biol 2000; 7: 601-20.
16. Shmulevich I, Dougherty ER, Kim S, Zhang W. Probabilistic boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 2002; 18: 261-74.
17. Ben-Dor A, Shamir R, Yakhini Z. Clustering gene expression patterns. J Comp Biol 1999; 6: 281-97.
18. Dougherty ER, Barrera J, Brun M, et al. Inference from clustering with application to gene-expression microarrays. J Comp Biol 2002; 9: 105-26.
19. Roden JC, King BW, Trout D, Mortazavi A, Wold BJ, Hart CE. Mining gene expression data by inter-preting principal components. BMC Bioinformatics 2006; 7: 194.
20. Dietterich TG. Approximate statistical test for comparing supervised classification learning algorithms. Neura\l Comp 1998; 10: 1895-923.
21. Martin JK, Hirschberg DS. Small sample statistics for classification error rates II: Confidence intervals and significance tests. Technical Report ICS-TR-96-22, University of California: Irvine 1996.
22. Bouckaert R, Frank E. Evaluating the replicability of significance tests for comparing learning algorithms. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Sydney, Australia 2005; pp. 3-12.
23. Berrar D, Bradbury I, Dubitzky W. Avoiding model selection bias in small-sample genomic datasets. Bioinformatics 2006; 22: 1245-50.
24. Nadeau C, Bengio Y. Inference for the generalization error. Mach Learn 2005; 52: 239-81.
25. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Int Joint Conferences on Artif Intell. San Mateo 1995; pp. 1137-45.
26. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA 2001; 98: 13790-5.