Fuzzy Logic for Elimination of Redundant Information of Microarray Data

Genomics, Proteomics and Bioinformatics

Volumn 6, Issue 2, 2008, Pages 61-73

  Huerta, EdmundoBonilla ^a   Duval, Béatrice ^a   Hao, Jin Kao ^a  

^a UNIVERSITÉ D'ANGERS   (France)

Author keywords

classification; dimension reduction; fuzzy processing; gene selection

Indexed keywords

ARTICLE; FUZZY LOGIC; FUZZY SYSTEM; GENE EXPRESSION; GENETIC SELECTION; LEUKEMIA; MICROARRAY ANALYSIS; NUCLEOTIDE SEQUENCE;

COLON; COLONIC NEOPLASMS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; DATABASES, GENETIC; FUZZY LOGIC; GENE EXPRESSION PROFILING; HUMANS; LEUKEMIA; LYMPHOMA; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 54349094943     PISSN: 16720229     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1672-0229(08)60021-2     Document Type: Article

References (49)

1. Alon U., et al. 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 (1999) 6745-6750
2. Ben-Dor A., et al. Tissue classification with gene expression profiles. J. Comput. Biol. 7 (2000) 559-583
3. Alizadeh A., et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403 (2000) 503-511
4. Golub T.R., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
5. Eisen M.B., et al. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95 (1998) 14863-14868
6. Xiong M., et al. Biomarker identification by feature wrappers. Genome Res. 11 (2001) 1878-1887
7. Jaeger J., et al. Improved gene selection for classification of microarrays. Pac. Symp. Biocomput. (2003) 53-64
8. Yu, L. and Liu, H. 2004. Redundancy based feature selection for microarray data. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds. Kim, W., et al.), pp. 737-742. Seattle, USA.
9. Marohnic, V., et al. 2004. Mutual information based reduction of data mining dimensionality in gene expression analysis. In Proceedings of the 26th International Conference on Information Technology Interfaces, Vol. 1, pp. 249-254. Cavtat, Croatia.
10. Ding C., and Peng H. Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3 (2005) 185-205
11. Liu X., et al. An entropy-based gene selection method for cancer classification using microarray data. BMC Bioinformatics 6 (2005) 76
12. Peng H., et al. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27 (2005) 1226-1238
13. Hu, Y., et al. 2006. A novel microarray gene selection method based on consistency. In Proceedings of the Sixth International Conference on Hybrid Intelligent Systems. IEEE Computer Society.
14. Mao K., and Tang W. Correlation-based relevancy and redundancy measures for efficient gene selection. Lect. Notes Comput. Sci. 4774 (2007) 230-241
15. Kim Y.B., et al. A new maximum-relevance criterion for significant gene selection. Lect. Notes Comput. Sci. 4146 (2006) 71-80
16. Li J., et al. Optimal search-based gene subset selection for gene array cancer classification. IEEE Trans. Inf. Technol. Biomed. 11 (2007) 398-405
17. Mundra P.A., and Rajapakse J.C. SVM-RFE with relevancy and redundancy criteria for gene selection. Lect. Notes Comput. Sci. 4774 (2007) 242-252
18. Mamitsuka H. Selecting features in microarray classification using ROC curves. Pattern Recognit. 39 (2006) 2393-2404
19. John, G., et al. 1994. Irrelevant features and the subset selection problem. In Proceedings of the 11th International Conference on Machine Learning, pp. 121-129. Morgan Kaufmann.
20. Yu L., and Liu H. Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5 (2004) 1205-1224
21. Saeys Y., et al. A review of feature selection techniques in bioinformatics. Bioinformatics 23 (2007) 2507-2517
22. Dudoit S., et al. Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc. 97 (2002) 77-87
23. Nguyen D.V., and Rocke D.M. Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18 (2002) 39-50
24. Ambroise C., and McLachlan G.J. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. USA 99 (2002) 6562-6566
25. Troyanskaya O., et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17 (2001) 520-525
26. Bicciato, S., et al. 2001. Analysis of an associative memory neural network for pattern identification in gene expression data. In Proceedings of the ACM SIGKDD Workshop on Data Mining in Bioinformatics, pp. 22-30. San Francisco, USA.
27. Cho, S.B. and Won, H.H. 2003. Machine learning in DNA microarray analysis for cancer classification. In Proceedings of the First Asia-Pacific Bioinformatics Conference, pp. 189-198. Adelaide, Australia.
28. Cho S.B., and Won H.H. Cancer classification using ensemble of neural networks with multiple significant gene subsets. Appl. Intell. 26 (2007) 243-250
29. Chu W., et al. Biomarker discovery in microarray gene expression data with Gaussian processes. Bioinformatics 21 (2005) 3385-3393
30. Tang Y., et al. FCM-SVM-RFE gene feature selection algorithm for leukemia classification from mi-croarray gene expression data. Proceedings of the 14th IEEE International Conference on Fuzzy Systems (2005), Reno, USA 97-101
31. Wang, Z., et al. 2006. Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. In Proceedings of the Second International Symposium on Evolving Fuzzy Systems, pp. 241-246. Lake District, UK.
32. Zhou X., et al. Gene selection using logistic regressions based on Aic, Bic and MDL criteria. New Math. Nat. Comput. 1 (2005) 129-145
33. Tang Y., et al. Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis. IEEE/ACM Trans. Comput. Biol. Bioinform. 4 (2007) 365-381
34. Guyon I., et al. Gene selection for cancer classification using support vector machines. Mach. Learn. 46 (2002) 389-422
35. Park P.J., et al. A nonparametric scoring algorithm for identifying informative genes from microarray data. Pac. Symp. Biocomput. (2001) 52-63
36. Furey T.S., et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16 (2000) 906-914
37. Li L., et al. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17 (2001) 1131-1142
38. Li W., and Yang Y. How many genes are needed for a discriminant microarray data analysis?. Methods of Microarray Data Analysis (2002), Kluwer Academic, Boston, USA 137-150
39. Marchiori E., and Sebag M. Bayesian learning with local support vector machines for cancer classification with gene expression data. Lect. Notes Comput. Sci. 3449 (2005) 74-83
40. Yang, W.H., et al. 2006. Generalized discriminant analysis for tumor classification with gene expression data. In Proceedings of the International Conference on Machine Learning and Cybernetics: 4322-4327.
41. Peng Y., et al. A hybrid approach for biomarker discovery from microarray gene expression data for cancer classification. Cancer Informatics 2 (2006) 301-311
42. Li, G.Z., et al. 2007. Partial least squares based dimension reduction with gene selection for tumor classification. In Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, pp. 1439-1444. Boston, USA.
43. Zhang L., et al. An effective gene selection method based on relevance analysis and discernibility matrix. Lect. Notes Comput. Sci. 4426 (2007) 1088-1095
44. Schuchhardt J., et al. Normalization strategies for cDNA microarrays. Nucleic Acids Res. 28 (2000) E47
45. Ross T.J. Fuzzy Logic with Engineering Applications. second edition (2005), Wiley
46. Tang C., et al. Interrelated clustering: an approach for gene expression data analysis. In: Wang J.T.L., et al. (Ed). Computational Biology and Genome Informatics (2003), World Scientific, Singapore 183-206
47. Shannon C.E. A mathematical theory of communication. Bell Syst. Tech. J. 27 (1948) 379-423 623-656.
48. Steuer R., et al. The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18 (2002) S231-S240
49. Schlögl A., et al. Estimating the mutual information of an EEG-based Brain-Computer Interface. Biomed. Tech. 47 (2002) 3-8