CFP last date
20 December 2024
Reseach Article

Review on Feature Selection Techniques of DNA Microarray Data

by Ammu P K, Preeja V
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 12
Year of Publication: 2013
Authors: Ammu P K, Preeja V
10.5120/9983-4814

Ammu P K, Preeja V . Review on Feature Selection Techniques of DNA Microarray Data. International Journal of Computer Applications. 61, 12 ( January 2013), 39-44. DOI=10.5120/9983-4814

@article{ 10.5120/9983-4814,
author = { Ammu P K, Preeja V },
title = { Review on Feature Selection Techniques of DNA Microarray Data },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 12 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number12/9983-4814/ },
doi = { 10.5120/9983-4814 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:57.365194+05:30
%A Ammu P K
%A Preeja V
%T Review on Feature Selection Techniques of DNA Microarray Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 12
%P 39-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Feature selection from DNA microarray data is one of the most important procedures in bioinformatics. The huge dimensionality of the DNA microarray data becomes a problem when it is used for cancer classification. This problem can be alleviated by employing feature selection as a preprocessing step in classification. This paper reviews some of the major feature selection techniques employed in microarray data and points out the merits and demerits of various approaches.

References
  1. Raychaudhuri, S. , Stuart, J. M. , and Altman, R. B. 2000. Principal components analysis to summarize microarray experiments: application to sporulation time series. In Pacific Symposium on Biocomputing. 455-466.
  2. Shannon, W. , Culverhouse, R. , and Duncan, J. 2003. Analyzing Microarray Data using Cluster Analysis. Pharmacogenomics. 4(1). 41-52.
  3. Wang, Z. 2005. Neuro-Fuzzy Modeling for Microarray Cancer Gene Expression Data. In Proceedings of the Second International Symposium on Evolving Fuzzy Systems. 241 – 246.
  4. 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 on Bioinformatics. 189-198.
  5. Fung, Y. M. , and Ng, V. T. Y. 2003. Classification of Heterogeneous Gene Expression Data. Article. 5(2). 69-78.
  6. Chiang, Y. M. , Lin, S. Y. 2008. The application of ant colony optimization for gene selection in microarray-based cancer classification. In Proceedings of the Seventh International Conference on Machine Learning and Cybernetics. 12-15.
  7. Nikumbh, S. , Ghosh, S. , and Jayaraman, V. K. 2012. Biogeography-Based Informative Gene Selection and Cancer Classification Using SVM and Random Forests. In IEEE Congress on Evolutionary Computation. 1-6.
  8. Karzynski, M. , Mateos, A. , and Dopazo, J. 2003. Using a Genetic Algorithm and a Perceptron for Feature Selection and Supervised Class Learning in DNA Microarray Data. Artificial intelligence review. 20(1). 39 – 51.
  9. Yu, Y. SVM-RFE Algorithm for Gene Feature Selection. Technical report. University of Delaware.
  10. Ding, C. , and Peng, H. Minimum Redundancy Feature Selection from microarray gene expression data. J Bioinform Comput Biol. 523—529.
  11. Yu, L. , and Liu, H. 2004. Redundancy Based Feature Selection for Microarray Data. In Proceedings of SIGKDD. 737-742.
  12. Ali, M. L. , 2005. Feature Selection of DNA Microarray Data. University of Windsor.
  13. Duin, R. P. , Jain, W. , Jain, A. K . , and Mao, J. 2000. Statistical Pattern Recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(1). 4-37.
  14. Simon, D. 2008. Biogeography Based Optimization. IEEE transactions on evolutionary computation. 12(6). 702-713.
  15. Kennedy, J. , and Eberhart, R. C. 1995. Particle swarm optimization. In IEEE International Conference on Neural Networks. 1942 - 1948
  16. Chen, F. , Zeng, X. Q. , Li, G. Z. et al. Redundant Gene Selection based on particle swarm optimization. In Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 10-16
  17. Zhang, L. J. , Li, Z. J. , and Hu, X. H. A Hybrid Gene Selection Method for Cancer Classification. In Proceedings of 2004 international conference on Machine Learning and Cybernetics. 2537 – 2542.
  18. George, G. , and Raj, V. C. Review on feature selection techniques and the impact of svm for cancer classification using gene expression profile, International Journal of Computer Science & Engineering Survey. 2(3). 16-27.
  19. Ryu, J. , and Cho, S. B. Towards Optimal Feature and Classifier for Gene Expression Classification of Cancer. In Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. 310-317.
Index Terms

Computer Science
Information Sciences

Keywords

Microarray DNA Feature Selection Cancer classification