Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014 (2014), Article ID 195470, 9 pages
http://dx.doi.org/10.1155/2014/195470
Research Article

A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

1Department of Computer Science and Engineering, RMK Engineering College, Anna University, Chennai, India
2Department of Information Science and Technology, Anna University, Chennai, India

Received 22 January 2014; Revised 20 June 2014; Accepted 2 July 2014; Published 6 August 2014

Academic Editor: Liyuan Li

Copyright © 2014 Jaison Bennet et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Causton, J. Quackenbush, and A. Brazma, Microarray Gene Expression Data Analysis: A Beginners Guide, Wiley-Blackwell, 2003.
  2. E. Domany, “Cluster analysis of gene expression data,” Journal of Statistical Physics, vol. 110, no. 3–6, pp. 1117–1139, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Maji and S. K. Pal, Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging, John Wiley & Sons, 2012.
  4. R. R. Coifman and M. V. Wickerhauser, “Entropy-based algorithms for best basis selection,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 713–718, 1992. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  5. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler, “Support vector machine classification and validation of cancer tissue samples using microarray expression data,” Bioinformatics, vol. 16, no. 10, pp. 906–914, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Statnikov, C. F. Aliferis, I. Tsamardinos, D. Hardin, and S. Levy, “A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis,” Bioinformatics, vol. 21, no. 5, pp. 631–643, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. O. Kwon and J. M. Sim, “Effects of data set features on the performances of classification algorithms,” Expert Systems with Applications, vol. 40, no. 5, pp. 1847–1857, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. Q. He and D. Lin, “A variable selection method for genome-wide association studies,” Bioinformatics, vol. 27, no. 1, pp. 1–8, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. D. S. Huang and C. H. Zheng, “Independent component analysis-based penalized discriminant method for tumor classification using gene expression data,” Bioinformatics, vol. 22, no. 15, pp. 1855–1862, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Liu, “Wavelet feature extraction for high-dimensional microarray data,” Neurocomputing, vol. 72, no. 4-6, pp. 985–990, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Li, C. Liao, and J. T. Kwok, “Wavelet-based feature extraction for microarray data classification,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '06), pp. 5028–5033, Vancouver, Canada, July 2006. View at Scopus
  13. L. Nanni and A. Lumini, “Wavelet selection for disease classification by DNA microarray data,” Expert Systems with Applications, vol. 38, no. 1, pp. 990–995, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Liu, “Detect key gene information in classification of microarray data,” EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 612397, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. S. L. Pomeroy, P. Tamayo, M. Gaasenbeek et al., “Prediction of central nervous system embryonal tumour outcome based on gene expression,” Nature, vol. 415, no. 6870, pp. 436–442, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. E. F. Petricoin III, A. M. Ardekani, B. A. Hitt et al., “Use of proteomic patterns in serum to identify ovarian cancer,” The Lancet, vol. 359, no. 9306, pp. 572–577, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. J. G. Zhang and H. W. Deng, “Gene selection for classification of microarray data based on the Bayes error,” BMC Bioinformatics, vol. 8, no. 1, pp. 370–379, 2007. View at Publisher · View at Google Scholar · View at Scopus