Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2013 (2013), Article ID 393570, 7 pages
http://dx.doi.org/10.1155/2013/393570
Research Article

A Robust Hybrid Approach Based on Estimation of Distribution Algorithm and Support Vector Machine for Hunting Candidate Disease Genes

1Devision of Medical Genetics, Tongji University School of Medicine, Shanghai 200092, China
2Key Lab for Basic Research in Cardiology, Ministry of Education, Tongji University, Shanghai 200092, China

Received 23 October 2012; Accepted 25 November 2012

Academic Editors: R. Jiang, W. Tian, J. Wan, and X. Zhao

Copyright © 2013 Li Li 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. W. Yang, D. Ying, and Y. L. Lau, “In-depth cDNA library sequencing provides quantitative gene expression profiling in cancer biomarker discovery,” Genomics, Proteomics and Bioinformatics, vol. 7, no. 1-2, pp. 1–12, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. S. S. Shen-Orr, R. Tibshirani, P. Khatri et al., “Cell type-specific gene expression differences in complex tissues,” Nature Methods, vol. 7, no. 4, pp. 287–289, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. A. A. Alizadeh, M. B. Elsen, R. E. Davis et al., “Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling,” Nature, vol. 403, no. 6769, pp. 503–511, 2000. View at Publisher · View at Google Scholar · View at Scopus
  4. P. J. Park, M. Pagano, and M. Bonetti, “A nonparametric scoring algorithm for identifying informative genes from microarray data,” Pacific Symposium on Biocomputing, pp. 52–63, 2001. View at Google Scholar · View at Scopus
  5. Y. Su, T. M. Murali, V. Pavlovic, M. Schaffer, and S. Kasif, “RankGene: Identification of diagnostic genes based on expression data,” Bioinformatics, vol. 19, no. 12, pp. 1578–1579, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Kahavi and G. H. John, “Wrapper for feature subset selection,” Artificial Intelligence, vol. 97, pp. 273–324, 1997. View at Google Scholar
  7. X. Li, S. Rao, Y. Wang, and B. Gong, “Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling,” Nucleic Acids Research, vol. 32, no. 9, pp. 2685–2694, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. S. J. Cho and M. A. Hermsmeier, “Genetic algorithm guided selection: variable selection and subset selection,” Journal of Chemical Information and Computer Sciences, vol. 42, no. 4, pp. 927–936, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. X. M. Zhao, Y. M. Cheung, and D. S. Huang, “A novel approach to extracting features from motif content and protein composition for protein sequence classification,” Neural Networks, vol. 18, no. 8, pp. 1019–1028, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Li, T. A. Darden, C. R. Weinberg, A. J. Levine, and L. G. Pedersen, “Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method,” Combinatorial Chemistry and High Throughput Screening, vol. 4, no. 8, pp. 727–739, 2001. View at Google Scholar · View at Scopus
  11. L. Li, W. Jiang, X. Li et al., “A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset,” Genomics, vol. 85, no. 1, pp. 16–23, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Saeys, S. Degroeve, D. Aeyels, P. Rouzé, and Y. Van de Peer, “Feature selection for splice site prediction: a new method using EDA-based feature ranking,” BMC Bioinformatics, vol. 5, p. 64, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. M. P. S. Brown, W. N. Grundy, D. Lin et al., “Knowledge-based analysis of microarray gene expression data by using support vector machines,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 1, pp. 262–267, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. J. H. Oh and J. Gao, “A kernel-based approach for detecting outliers of high-dimensional biological data,” BMC Bioinformatics, vol. 10, supplement 4, p. S7, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Hua and Z. Sun, “A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach,” Journal of Molecular Biology, vol. 308, no. 2, pp. 397–407, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Zhu, X. Shen, and W. Pan, “Network-based support vector machine for classification of microarray samples,” BMC Bioinformatics, vol. 10, supplement 1, p. S21, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Evers and C. M. Messow, “Sparse kernel methods for high-dimensional survival data,” Bioinformatics, vol. 24, no. 14, pp. 1632–1638, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. V. Vapnik, Statistical Learning Theory, Wiley, New York, NY, USA, 1998.
  19. C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998. View at Google Scholar · View at Scopus
  20. U. Alon, N. Barka, D. A. Notterman et al., “Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 12, pp. 6745–6750, 1999. View at Publisher · View at Google Scholar · View at Scopus
  21. T. K. Paul and H. Iba, “Gene selection for classification of cancers using probabilistic model building genetic algorithm,” BioSystems, vol. 82, no. 3, pp. 208–225, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. O. Troyanskaya, M. Cantor, G. Sherlock et al., “Missing value estimation methods for DNA microarrays,” Bioinformatics, vol. 17, no. 6, pp. 520–525, 2001. View at Google Scholar · View at Scopus
  23. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology. The Gene Ontology Consortium,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, and M. Hattori, “The KEGG resource for deciphering the genome,” Nucleic Acids Research, vol. 32, pp. D277–D280, 2004. View at Google Scholar · View at Scopus
  25. M. Kanehisa, S. Goto, S. Kawashima, and A. Nakaya, “Thed KEGG databases at GenomeNet,” Nucleic Acids Research, vol. 30, no. 1, pp. 42–46, 2002. View at Google Scholar · View at Scopus
  26. G. Lenz, G. W. Wright, N. C. T. Emre et al., “Molecular subtypes of diffuse large B-cell lymphoma arise by distinct genetic pathways,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 36, pp. 13520–13525, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. R. E. Davis, K. D. Brown, U. Siebenlist, and L. M. Staudt, “Constitutive nuclear factor kappaB activity is required for survival of activated B cell-like diffuse large B cell lymphoma cells,” The Journal of Experimental Medicine, vol. 194, pp. 1861–1874, 2001. View at Google Scholar
  28. I. S. Lossos, D. K. Czerwinski, A. A. Alizadeh et al., “Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes,” The New England Journal of Medicine, vol. 350, no. 18, pp. 1828–1837, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Amenta, M. Moschovi, C. Sofocleous, S. Kostaridou, A. Mavrou, and H. Fryssira, “Non-Hodgkin lymphoma in a child with Williams syndrome,” Cancer Genetics and Cytogenetics, vol. 154, no. 1, pp. 86–88, 2004. View at Publisher · View at Google Scholar · View at Scopus
  30. C. H. Lawrie, S. Soneji, T. Marafioti et al., “MicroRNA expression distinguishes between germinal center B cell-like and activated B cell-like subtypes of diffuse large B cell lymphoma,” International Journal of Cancer, vol. 121, no. 5, pp. 1156–1161, 2007. View at Publisher · View at Google Scholar · View at Scopus