An efficient SVM based tumor classification with symmetry Non-negative Matrix Factorization using gene expression data

2013 International Conference on Information Communication and Embedded Systems, ICICES 2013

Volumn , Issue , 2013, Pages 761-768

  Yuvaraj, N ^a   Vivekanandan, P ^a  

^a Park College of Engineering and Technology ^*  (India)

Author keywords

DNA microarray; Nonnegative Matrix Factorization (NMF); Support Vector Machine with Weighted Kernel Width (WSVM); Symmetry NMF (SymNMF)

Indexed keywords

CANCER CLASSIFICATION; CLASSIFICATION APPROACH; DNA MICRO-ARRAY; GENE EXPRESSION DATA; MACHINE LEARNING APPROACHES; NONNEGATIVE MATRIX FACTORIZATION; TRADITIONAL APPROACHES; TUMOR CLASSIFICATION;

BIOASSAY; BIOCHIPS; COMMUNICATION; DISEASES; EMBEDDED SYSTEMS; FACTORIZATION; SUPPORT VECTOR MACHINES; TUMORS;

GENE EXPRESSION;

EID: 84879861294     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICICES.2013.6508193     Document Type: Conference Paper

References (32)

1. P.O. Brown and D. Botstein, "Exploring the New World of the Genome with DNA Microarray," The Chipping Forest, vol. 21, pp. 33-37, 1999
2. E.E. Ntzani and J.P. Ioannidis, "Predictive Ability of DNA Microarrays for Cancer Outcomes and Correlates: An Empirical Assessment," Lancet, vol. 362, pp. 1439-1444, 2003
3. T.R. Golub et al., "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring," Science, vol. 286, pp. 531-537, 1999
4. Chun-Hou Zheng, To-Yee Ng, Lei Zhang, Chi-Keung Shiu, and Hong-Qiang Wang, ''Tumor Classification Based on Non-Negative Matrix Factorization Using Gene Expression Data'', IEEE Transactions on Nanobioscience, Vol. 10, No. 2, 2011
5. N.Dasgupta, S. Lin, and L. Carin, "Sequentialmodeling for identifying CpG island locations in human genome," IEEE Signal Process. Lett., vol. 9, no. 12, pp. 407-409, 2002
6. G. R. Ridgway and S. J. Godsill, "Bayesian image modeling of cDNA microarray spots," IEEE Signal Process. Lett., vol. 14, no. 10, pp. 653-656, 2007
7. S. Y. Wang, F. C. Tian, X. Liu, and J. Wang, "A novel representation approach to DNA sequence and its application," IEEE Signal Process. Lett., vol. 16, no. 4, pp. 275-278, 2009
8. N. Pochet, F. De Smet, J. A. K. Suykens, and B. L. R. De Moor, "Systematic benchmarking of microarray data classification: Assessing the role of non-linearity and dimensionality reduction," Bioinformatics, vol. 20, pp. 3185-3195, 2004
9. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander, ''Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring,'' Science, vol. 286, pp. 531-537, 1999
10. J. G. Liao and K. V. Chin, ''Logistic regression for disease classification using microarray data: model selection in a large p and small n case," Bioinformatics, vol. 23, no. 15, pp. 1945-1951, 2007
11. H. H. Zhang, J. Ahn, X. Lin, and C. Park, "Gene selection using support vector machines with non-convex penalty," Bioinformatics, vol. 22, pp. 88-95, 2006
12. S. Dudoit, J. Fridlyand, and T. P. Speed, "Comparison of discrimination methods for classification of tumor using gene expression data," J.Amer. Stat. Assoc, vol. 97, pp. 77-87, 2002
13. T.R. Golub et al., "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring," Science, vol. 286, pp. 531-537, 1999
14. D.S. Huang and C.H. Zheng, "Independent Component Analysis-Based Penalized Discriminant Method for Tumor Classification Using Gene Expression Data," Bioinformatics, vol. 22, pp. 1855-1862, 2006
15. J. Reunanen, "Overfitting in Making Comparisons between Variable Selection Methods," J. Machine Learning Research, vol. 3, pp. 1371-1382, 2003
16. D. V. Nguyen and D. M. Rocke, "Tumor classification by partial least squares using microarray gene expression data," Bioinformatics, vol. 18, no. 1, pp. 39-50, 2002
17. D. S. Huang and C. H. Zheng, "Independent component analysis-based penalized discriminant method for tumor classification using gene expression data," Bioinformatics, vol. 22, pp. 1855-1862, 2006
18. M. West, C. Blanchette, H. Dressman, E. Huang, S. Ishida, R. Spang, H. Zuzan, J. A. Olson, J. R. Marks, and J. R. Nevins, "Predicting the clinical status of human breast cancer by using gene expression profiles," Proc. Natl. Acad. Sci. USA, vol. 98, pp. 11462-11467, 2001
19. O. Alter, P. Browna, and D. Botstein, "Singular value decomposition for genome-wide expression data processing and modeling," Proc. Natl. Acad. Sci. USA, vol. 97, pp. 10101-10106, 2000
20. P. Comon, "Independent component analysis-A new concept?," Signal Process., vol. 36, pp. 287-314, 1994
21. D. S. Huang and C. H. Zheng, "Independent component analysis-based penalized discriminant method for tumor classification using gene expression data," Bioinformatics, vol. 22, pp. 1855-1862, 2006
22. J.-P. Brunet, P. Tamayo, T. R. Golun, and J. P. Mesirov, "Metagenes and molecular pattern discovery using matrix factorization," Proc. Natl. Acad. Sci. USA, vol. 101, no. 12, pp. 4164-416, 2004
23. Y. Gao and C. George, "Improving molecular cancer class discovery through sparse non-negative matrix factorization," Bioinformatics, vol. 21, pp. 3970-3975, 2005
24. C. Ding, X. He, and H. D. Simon, "On the equivalence of nonnegative matrix factorization and spectral clustering," in SDM '05: Proc. of SIAM Int. Conf. on Data Mining, 2005, pp. 606-610
25. M. Catral, L. Han, M. Neumann, and R. J. Plemmons, "On reduced rank nonnegative matrix factorization for symmetric matrices," Linear Algebra and Its Applications, vol. 393, pp. 107-126, 2004
26. D. D. Lee and H. S. Seung, "Learning the parts of objects by nonnegative matrix factorization," Nature, vol. 401, pp. 788-791, 1999
27. C. Ding, X. He, and H. D. Simon, "On the equivalence of nonnegative matrix factorization and spectral clustering," in SDM '05: Proc. of SIAM Int. Conf. on Data Mining, 2005, pp. 606-610
28. S. Z. Li, X. W. Hou, H. J. Zhang, and Q. S. Cheng, "Learning spatially localized parts-based representation," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recog., 2001, vol. 1, pp. 207-212
29. D. S. Huang and C. H. Zheng, "Independent component analysis-based penalized discriminant method for tumor classification using gene expression data," Bioinformatics, vol. 22, pp. 1855-1862, 2006
30. Vladimir N. Vapnik. Statistical Learning Theory. New York: Wiley, 1998
31. 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 arrays," Proc. Natl. Acad. Sci. USA, vol. 96, pp. 6745-6750, 1999
32. J.-P. Brunet, P. Tamayo, T. R. Golun, and J. P. Mesirov, "Metagenes and molecular pattern discovery using matrix factorization," Proc. Natl. Acad. Sci. USA, vol. 101, no. 12, pp. 4164-416, 2004