A hybrid LDA and genetic algorithm for gene selection and classification of microarray data

Neurocomputing

Volumn 73, Issue 13-15, 2010, Pages 2375-2383

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

^a Instituto Tecnologico de Apizaco   (Mexico)

^b UNIVERSITÉ D'ANGERS   (France)

Author keywords

Classification; Dedicated genetic algorithm; Gene selection; Linear discriminant analysis

Indexed keywords

COMPUTATIONAL EXPERIMENT; CROSSOVER AND MUTATION; DATA SETS; EXPERIMENTAL PROTOCOLS; FISHER'S LINEAR DISCRIMINANT ANALYSIS; FITNESS FUNCTIONS; GA ALGORITHM; GENE SELECTION; INFORMATIVE GENES; LDA CLASSIFIERS; LINEAR DISCRIMINANT ANALYSIS; MICROARRAY DATA; PREDICTION ACCURACY; PREDICTIVE ACCURACY; SUPERVISED CLASSIFICATION;

DISCRIMINANT ANALYSIS; FUNCTION EVALUATION; GENES; MATHEMATICAL OPERATORS;

GENETIC ALGORITHMS;

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; COMPUTER PREDICTION; CROSSING OVER; DISCRIMINANT ANALYSIS; FISHER LINEAR DISCRIMINANT ANALYSIS; GENETIC ALGORITHM; GENETIC DATABASE; GENETIC SELECTION; MATHEMATICAL MODEL; MICROARRAY ANALYSIS; MUTATIONAL ANALYSIS; PRIORITY JOURNAL;

EID: 77955334667     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.03.024     Document Type: Article

References (43)

1. Alizadeh A., Eisen M.B., et al. Distinct types of diffuse large (b)-cell lymphoma identified by gene expression profiling. Nature 2000, 403(February):503-511.
2. Allison D.B., Cui X., Page G.P., Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nature Reviews Genetics 2006, 7(1):55-65.
3. Alon U., Barkai N., 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 1999, 96:6745-6750.
4. Ambroise C., McLachlan G. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. USA 2002, 99(10):6562-6566.
5. Ben-Dor A., Bruhn L., et al. Tissue classification with gene expression profiles. Journal of Computational Biology 2000, 7(3-4):559-583.
6. Bonilla Huerta E., Duval B., Hao J.K. A hybrid GA/SVM approach for gene selection and classification of microarray data. Applications of Evolutionary Computing, EvoWorkshops 2006, Lecture Notes in Computer Science 2006, vol. 3907:34-44. Springer.
7. Bonilla Huerta E., Duval B., Hao J.K. Fuzzy logic for elimination of redundant information of microarray data. Genomics, Proteomics and Bioinformatics June 2008, 6(2):61-73.
8. Braga-Neto U., Dougherty E.R. Is cross-validation valid for small-sample microarray classification?. Bioinformatics 2004, 20(3):374-380.
9. Cho S.-B., Won H.-H. Cancer classification using ensemble of neural networks with multiple significant gene subsets. Applied Intelligence 2007, 26(3):243-250.
10. Ding C., Peng H. Minimum redundancy feature selection from microarray gene expression data. Bioinformatics and Computational Biology 2005, 3(2):185-206.
11. 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 2002, 97:77-87.
12. Efron B. Estimating the error rate of a prediction rule: improvement on cross-validation. Journal of the American Statistical Association 1983, 78(382):316-331.
13. Furey T., Cristianini N., et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16(10):906-914.
14. Golub T., Slonim D., Loh M., Downing J., Caligiuri M., Bloomfield C., Lander E., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537.
15. Gordon G.J., Jensen R.V., Ramaswamy S., Richards W.G., Sugarbaker D.J., Bueno R., et al. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research 2002, 17(62):4963-4967.
16. Hernandez Hernandez J.C., Duval B., Hao J.K. A genetic embedded approach for gene selection and classification of microarray data. EvoBIO'07, Lecture Notes in Computer Science 2007, vol. 4447:90-101. Springer.
17. Kim K.-J., Cho S.-B. Prediction of colon cancer using an evolutionary neural network. Neurocomputing 2004, 61:361-379.
18. R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection, in: S. Mateo (Eds.), Proceedings of the 14th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, CA, 1995, pp. 1137-1143.
19. Lee S. Mistakes in validating the accuracy of a prediction classifier in high-dimensional but small-sample microarray data. Statistical Methods in Medical Research 2008, 17:635-642.
20. Li L., Weinberg C.R., Darden T.A., Pedersen L.G. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the ga/knn method. Bioinformatics 2001, 17(12):1131-1142.
21. Li L., Jiang W., Li X., Moser K.L., Guo Z., Du L., Wang Q., Topol E.J., Wang Q., Rao S. A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics 2005, 85:16-23.
22. G.Z. Li, X.Q. Zeng, J.Y. Yang, M.Q. Yang, Partial least squares based dimension reduction with gene selection for tumor classification, in: Proceedings of IEEE Seventh International Symposium on Bioinformatics and Bioengineering, 2007, pp. 1439-1444.
23. Li S., Wu X., Hu X. Gene selection using genetic algorithm and support vectors machines. Soft Computing 2008, 12(7):693-698.
24. 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 2004, 5:136(138):1-12.
25. Liu J.J., Cutler G., Li W., Pan Z., Peng S., Hoey T., Chen L., Ling X.B. Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics 2005, 21(11):2691-2697.
26. Marchiori E., Sebag M. Bayesian learning with local support vector machines for cancer classification with gene expression data. EvoWorkshops, Lecture Notes in Computer Science 2005, vol. 3449. Springer, pp. 74-83.
27. Ooi C.H., Tan P. Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 2003, 19(1):37-44.
28. Pang S., Havukkala I., Hu Y., Kasabov N. Classification consistency analysis for bootstrapping gene selection. Neural Computing and Applications 2007, 16:527-539.
29. Park H., Park C. A comparison of generalized linear discriminant analysis algorithms. Pattern Recognition 2008, 41(3):1083-1097.
30. Peng S., Xu Q., Ling X.B., Peng X., Du W., Chen L. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Letters 2003, 555(2):358-362.
31. Peng Y., Li W., Liu Y. A hybrid approach for biomarker discovery from microarray gene expression data. Cancer Informatics 2006, 2:301-311.
32. Petricoin E.F., Ardekani A.M., Hitt B.A., Levine P.J., Fusaro V.A., Steinberg S.M., Mills G.B., Simone C., Fishman D.A., Kohn E.C., Liotta L.A. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002, 359:572-577.
33. Pomeroy S.L., Tamayo P., et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 2002, 415:436-442.
34. Simon R., Radmacher M.D., Dobbin K., McShane L.M. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute January 2003, 95(1):14-18.
35. Singh D., Febbo P., Ross K., Jackson D., Manola J., Ladd C., Tamayo P., Renshaw A., D'Amico A., Richie J. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002, 1:203-209.
36. Tan A.C., Gilbert D. Ensemble machine learning on gene expression data for cancer classification. Applied Bioinformatics 2003, 2(2):75-83.
37. Z. Wang, V. Palade, Y. Xu, Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis, in: Proceedings of the Evolving Fuzzy Systems, 2006, pp. 241-246.
38. Yang W.-H., Dai D.-Q., Yan H. Generalized discriminant analysis for tumor classification with gene expression data. Machine Learning and Cybernetics 2006, 1:4322-4327.
39. Ye J. Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. Journal of Machine Learning Research 2005, 6:483-502.
40. Ye J., Li T., Xiong T., Janardan R. Using uncorrelated discriminant analysis for tissue classification with gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2004, 1(4):181-190.
41. Yue F., Wang K., Zuo W. Informative gene selection and tumor classification by null space LDA for microarray data. ESCAPE'07, Lecture Notes in Computer Science 2007, vol. 4614. Springer, pp. 435-446.
42. L. Zhang, Z. Li, H. Chen, An effective gene selection method based on relevance analysis and discernibility matrix, in: PAKDD, Lecture Notes in Computer Science, vol. 4426, 2007, pp. 1088-1095.
43. Zhu Z., Ong Y.S., Dash M. Markov blanket-embedded genetic algorithm for gene selection. Pattern Recognition 2007, 40(11):3236-3248.