Gene selection for microarray data by a LDA-based genetic algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5265 LNBI, Issue , 2008, Pages 250-261

  Huerta, Edmundo Bonilla ^a   Duval, Béatrice ^a   Hao, Jin Kao ^a  

^a UNIVERSITÉ D'ANGERS   (France)

Author keywords

Classification; Gene selection; Genetic algorithm; Linear discriminant analysis; Wrapper

Indexed keywords

BIOINFORMATICS; COMPUTER AIDED DIAGNOSIS; DIESEL ENGINES; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; FUNCTION EVALUATION; GENES; IMAGE RETRIEVAL; MATHEMATICAL OPERATORS; PATTERN RECOGNITION;

CLASSIFICATION; CLASSIFICATION ACCURACIES; CROSSOVER AND MUTATIONS; DATA SETS; FISHER'S LINEAR DISCRIMINANT ANALYSES; FITNESS FUNCTIONS; GENE SELECTION; GENE SELECTIONS; INFORMATIVE GENES; LDA CLASSIFIERS; LINEAR DISCRIMINANT ANALYSIS; MICROARRAY DATUMS; PREDICTIVE ACCURACIES; WRAPPER; WRAPPER APPROACHES;

GENETIC ALGORITHMS;

EID: 57049136560     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88436-1-22     Document Type: Conference Paper

References (29)

1. Alizadeh, A., Eisen, B.M., Davis, R.E., et al.: Distinct types of diffuse large (b)-cell lymphoma identified by gene expression profiling. Nature 403, 503-511 (2000)
2. Alon, U., Barkai, N., Notterman, D., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Nat. Acad. Sci. USA. 96, 6745-6750 (1999)
3. Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., Yakhini, Z.: Tissue classification with gene expression profiles. Journal of Computational Biology 7(3-4), 559-583 (2000)
4. Bonilla Huerta, E., Duval, B., Hao, J.K.: A hybrid ga/svm approach for gene selection and classification of microarray data. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 34-44. Springer, Heidelberg (2006)
5. Bonilla Huerta, E., Duval, B., Hao, J.K.: Fuzzy logic for elimination of redundant information of microarray data. In: Genomics, Proteomics and Bioinformatics (June 2008) (to appear)
6. Cho, S.-B., Won, H.-H.: Cancer classification using ensemble of neural networks with multiple significant gene subsets. Applied Intelligence 26(3), 243-250 (2007)
7. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinformatics and Computational Biology 3(2), 185-206 (2005)
8. Dudoit, S., Fridlyand, J., Speed, T.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97, 77-87 (2002)
9. Golub, T., Slonim, D., Tamayo, P., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531-537 (1999)
10. Gordon, G. J., et al.: Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma, Cancer Res. 62, 4963 (2002)
11. Hernandez Hernandez, J.C., Duval, B., Hao, J.K.: A genetic embedded approach for gene selection and classification of microarray data. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds.) EvoBIO 2007. LNCS, vol. 4447, pp. 90-101. Springer, Heidelberg (2007)
12. Li, G.-Z., Zeng, X.-Q., Yang, J.Y., Yang, M.Q.: Partial least squares based dimension reduction with gene selection for tumor classification. In: Proc. of 7th IEEE Intl. Symposium on Bioinformatics and Bioengineering, pp. 1439-1444 (2007)
13. Li, S., Wu, X., Hu, X.: Gene selection using genetic algorithm and support vectors machines. Soft Comput. 12(7), 693-698 (2008)
14. Liu, B., Cui, Q., Jiang, T., Ma, S.: A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinformatics 5(136), 1-12 (2004)
15. Marchiori, E., Sebag, M.: Bayesian learning with local support vector machines for cancer classification with gene expression data. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 74-83. Springer, Heidelberg (2005)
16. Pang, S., Havukkala, I., Hu, Y., Kasabov, N.: Classification consistency analysis for bootstrapping gene selection. Neural Computing and Appli. 16, 527-539 (2007)
17. Park, H., Park, C.: A comparison of generalized linear discriminant analysis algorithms. Pattern Recognition 41(3), 1083-1097 (2008)
18. Peng, Y., Li, W., Liu, Y.: A hybrid approach for biomarker discovery from microarray gene expression data. Cancer Informatics, 301-311 (2006)
19. Petricoin, E.F., Ardekani, A.M., Hitt, B., Levine, P., Steinberg, S., Mills, G., Simone, C., Fishman, D., Kohn, E., Liotta, L.A.: Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572-577 (2002)
20. Pomeroy, S.L., Tamayo, P., Gaasenbeek, M., Sturla, L.M., Golub, T.R.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415(6870), 436-442 (2002)
21. Singh, D., Febbo, P.B., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., Amico, A.V., Richie, J.P., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203-209 (2002)
22. Tan, A.C., Gilbert, D.: Ensemble machine learning on gene expression data for cancer classification. Appl. Bioinformatics 2(3 Suppl), 75-83 (2003)
23. Wang, S., Chen, H., Li, S., Zhang, D.: Feature extraction from tumor gene expression profiles using DCT and DFT. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 485-496. Springer, Heidelberg (2007)
24. Wang, Z., Palade, V., Xu, Y.: Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis. In: Proc. Evolving Fuzzy Systems, pp. 241-246 (2006)
25. Yang, W.-H., Dai, D.-Q., Yan, H.: Generalized discriminant analysis for tumor classification with gene expression data. In: Machine Learning and Cybernetics, pp. 4322-4327 (2006)
26. Ye, J.: Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. Journal of Machine Learning Research 6, 483-502 (2005)
27. Ye, J., Li, T., Xiong, T., Janardan, R.: Using uncorrelated discriminant analysis for tissue classification with gene expression data. IEEE/ACM Trans. Comput. Biology Bioinform. 1(4), 181-190 (2004)
28. Yue, F., Wang, K., Zuo, W.: Informative gene selection and tumor classification by null space LDA for microarray data. In: Chen, B., Paterson, M., Zhang, G. (eds.) ESCAPE 2007. LNCS, vol. 4614, pp. 435-446. Springer, Heidelberg (2007)
29. Zhang, L., Li, Z., Chen, H.: An effective gene selection method based on relevance analysis and discernibility matrix. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 1088-1095. Springer, Heidelberg (2007)