Differential evolution algorithms for finding predictive gene subsets in microarray data

IFIP International Federation for Information Processing

Volumn 204, Issue , 2006, Pages 484-491

  Tasoulis, D K ^a   Plagianakos, V P ^a   Vrahatis, M N ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33749129565     PISSN: 15715736     EISSN: None     Source Type: Book Series    
DOI: 10.1007/0-387-34224-9_56     Document Type: Article

References (15)

1. U. Alon, N. Barkai, D.A. Notterman, K.Gish, S. Ybarra, D. Mack, and A.J. Levine. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide array. Proc, Natl. Acad, Sci. USA, 96(12):6745-6750,1999.
2. D. Singh et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 1:203-209, 2002.
3. J. Ilonen, J,-K. Kamarainen, and J. Lampinen. Differential evolution training algorithm for feed forward neural networks. Neural Processing Letters, 17(1):93-105, 2003.
4. D. Jiang, C. Tang, and A. Zhangi. Cluster analysis for gene expression data: A survey. IEEE Transactions on Knowledge and Data Engineering, to appear.
5. T. Li, C. Zhang, and M.Ogihara. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 20(15):2429-2437, 2004.
6. V.P. Plagianakos, D.K. Tasoulis, and M.N. Vrahatis. Hybrid dimension reduction approach for gene expression data classification. In Intemationai Joint Conference on Neural Networks 2005, Post-Conference Workshop on Computational Intelligence Approaches for the Analysis of Bioinformatics Data, 2005.
7. V.P. Plagianakos and M.N. Vrahatis. Neural network training with constrained integer weights. In P.J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, Proceedings of the Congress of Evolutionary Computation (CEC'99), pages 2007-2013. IEEE Press, 1999.
8. V.P. Plagianakos and M.N. Vrahatis. 'Training neural networks with 3-bit integer weights. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela, and R.E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'99), pages 910-915. Morgan Kaufoaann, 1999.
9. V.P. Plagianakos and M.N. Vrahatis. Parallel evolutionary training algorithms for 'hardware-friendly' neural networks. Natural Computing, 1:307-322, 2002.
10. M. Riedmiller and H. Braun. A direct adaptive method for faster backpropagation learning: The Rprop algorithm. In Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA, pages 586-591, 1993.
11. R. Storn and K. Price. Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization, 11:341-359, 1997.
12. D.K. Tasoulis, N.G. Pavlidis, V.P. Plagianakos, and M.N. Vrahatis. Parallel differential evolution. In IEEE Congress on Evolutionary Computation (CEC 2004), volume 2, pages 2023-2029, 2004.
13. D.K. Tasoulis, V.P. Plagianakos, and M.N. Vrahatis. Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima. In IEEE Congress on Evolutionary Computation, volume 2, pages 1847-1854, Edinburgh, UK, 2005.
14. M.E. Wall, A. Rechtsteiner, and L.M. Rocha. Singular value decomposition and principal component analysis. In A PracUcal Approach to Microarray Data Analysis, pages 91-109. Kluwer, 2003.
15. J. Ye, T. Li, T. Xiong, and R. Janardan. Using uncorrelated discriminant analysis for tissue classification with gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1(4):181-190, 2004.