A hybrid BPSO-CGA approach for gene selection and classification of microarray data

Journal of Computational Biology

Volumn 19, Issue 1, 2012, Pages 68-82

  Yang, Cheng Hong ^b,c   Chuang, Li Yeh ^a   Li, Jung Chike ^b   Yang, Cheng Huei ^b  

^a I SHOU UNIVERSITY   (Taiwan)

^b NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

^c TOKO UNIVERSITY   (Taiwan)

Author keywords

feature selection; genetic algorithm; K nearest neighbor; microarray; particle swarm optimization

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGY; CLASSIFICATION; CLUSTER ANALYSIS; DNA MICROARRAY; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETIC SELECTION; GENETIC VARIABILITY; GENETICS; HUMAN; METHODOLOGY;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETIC VARIATION; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SELECTION, GENETIC;

EID: 84855461005     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2010.0064     Document Type: Article

References (44)

1. AltIncay, H. 2007. Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation. Applied Soft Computing 7, 1072-1083. (Pubitemid 46551937)
2. Berrar, D., Bradbury, I., and Dubitzky, W. 2006. Instance-based concept learning from multiclass DNA microarray data. BMC Bioinformatics 7, 73.
3. Breiman, L. 1984. Classification and Regression Trees. Chapman & Hall/CRC, Boca Raton, FL.
4. Buturovic, L.J. 2006. PCP: a program for supervised classification of gene expression profiles. Bioinformatics 22, 245.
5. Chang, J.C., Hilsenbeck, S.G., and Fuqua, S.A.W. 2005. The promise of microarrays in the management and treatment of breast cancer. Breast Cancer Res. 7, 100.
6. Choudhary, A., Brun, M., Hua, J., et al. 2006. Genetic test bed for feature selection. Bioinformatics 22, 837.
7. Cover, T., and Hart, P. 1967. Nearest neighbor pattern classification. IEEE Trans. Information Theory 13, 21-27.
8. Cover, T.M., and Van Campenhout, J.M. 1977. On the possible orderings in the measurement selection problem. IEEE Trans. Syst. Man Cybernet. 7, 657-661. (Pubitemid 8164872)
9. Deutsch, J.M. 2003. Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics 19, 45.
10. Diaz-Uriarte, R., and de Andres, A. 2006. Gene selection and classification of microarray data using Random Forest. BMC bioinformatics 7, 3.
11. Dudoit, S., Fridlyand, J., and Speed, T.P. 2002. Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc. 97, 77-87.
12. Eksin, I., and Erol, O.K. 2001. Evolutionary algorithm with modifications in the reproduction phase. IEE Proc. Software 148, 75-80. (Pubitemid 32672350)
13. Erol, O.K., and Eksin, I. 2006. A new optimization method: big bang-big crunch. Adv. Eng. Software 37, 106-111. (Pubitemid 41543030)
14. Fix, E., and Hodges, Jr., J.L. 1989. Discriminatory analysis. Nonparametric discrimination: consistency properties. Int. Stat. Rev. 57, 238-247.
15. Forozan, F., Mahlamaki, E.H., Monni, O., et al. 2000. Comparative genomic hybridization analysis of 38 breast cancer cell lines: a basis for interpreting complementary DNA microarray data. Cancer Res. 60, 4519.
16. Furey, T.S., Cristianini, N., Duffy, N., et al. 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906.
17. Hastie, T., Tibshirani, R., Friedman, J., et al. 2005. The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer 27, 83-85.
18. Holland, J.H. 1992. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA.
19. Hua, J., Xiong, Z., Lowey, J., et al. 2005. Optimal number of features as a function of sample size for various classification rules. Bioinformatics 21, 1509.
20. Huang, H.L., and Chang, F.L. 2007. ESVM: evolutionary support vector machine for automatic feature selection and classification of microarray data. Biosystems 90, 516-528.
21. Jeffrey, S.S., Lonning, P.E., and Hillner, B.E. 2005. Genomics-based prognosis and therapeutic prediction in breast cancer. J. Natl. Comprehensive Cancer Network 3, 291-300. (Pubitemid 44265435)
22. Jirapech-Umpai, T., and Aitken, S. 2005. Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes. BMC Bioinformatics 6, 148.
23. Kao, Y.T., and Zahara, E. 2008. A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl. Soft Computing 8, 849-857. (Pubitemid 350266482)
24. Kennedy, J. 2006. Swarm Intelligence. Springer, New York.
25. Kennedy, J., and Eberhart, R. 1995. Particle swarm optimization. IEEE Int Conf. Neural Networks 4, 1942-1948
26. Kennedy, J., and Eberhart, R.C. 1997. A discrete binary version of the particle swarm algorithm. IEEE Int. Conf. Syst. Man Cybernet. 5, 4104-4108.
27. Lee, J.W., Lee, J.B., Park, M., et al. 2005. An extensive comparison of recent classification tools applied to microarray data. Comput. Stat. Data Anal. 48, 869-885.
28. Lee, Y., and Lee, C.K. 2003. Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 19, 1132.
29. Li, T., Zhang, C., and Ogihara, M. 2004. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20, 2429.
30. Liu, X., Krishnan, A., and Mondry, A. 2005. An entropy-based gene selection method for cancer classification using microarray data. BMC Bioinformatics 6, 76.
31. Lonning, P.E., Sorlie, T., and Borresen-Dale, A.L. 2005. Genomics in breast cancer-therapeutic implications. Nat. Clin. Pract. Oncol. 2, 26-33.
32. rd Genet. Programm. 469-476.
33. Oh, I.S., Lee, J.S., and Moon, B.R. 2004. Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 1424-1437.
34. Ramaswamy, S., Tamayo, P., Rifkin, R., et al. 2001. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98, 15149.
35. Ripley, B.D. 2008. Pattern Recognition and Neural Networks. Cambridge University Press, New York.
36. Sberveglieri, M.P.G. 2008. Random Forests and nearest shrunken centroids for the classification of sensor array data. Sensors Actuators B Chem. 131, 93-99.
37. Shi, Y., and Eberhart, R. 1998. A modified particle swarm optimizer. Proc. IEEE Int. Conf. Evol. Computation 69-73.
38. Sorensen, K., and Sevaux, M. 2006. MAj PM: memetic algorithms with population management. Computers Operations Res. 33, 1214-1225. (Pubitemid 41428287)
39. Tahir, M.A., Bouridane, A., and Kurugollu, F. 2007. Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier. Pattern Recogn. Lett. 28, 438-446. (Pubitemid 44918133)
40. Tahir, M.A., Bouridane, A., Kurugollu, F., et al. 2005. A novel prostate cancer classification technique using intermediate memory tabu search. EURASIP J. Appl. Signal Processing 14, 2241.
41. Tan, S. 2006. An effective refinement strategy for KNN text classifier. Expert Syst. Applications 30, 290-298. (Pubitemid 41745051)
42. Tibshirani, R., Hastie, T., Narasimhan, B., et al. 2002. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. USA 99, 6567.
43. Wang, X., Yang, J., Teng, X., et al. 2007. Feature selection based on rough sets and particle swarm optimization. Pattern Recogn. Lett. 28, 459-471. (Pubitemid 44920436)
44. Zhu, Z., Ong, Y., and Dash, M. 2007. Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans. Syst. Man. Cybernet. B 37, 70.