Performance comparison of tumor classification based on linear and non-linear dimensionality reduction methods

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6215 LNCS, Issue , 2010, Pages 291-300

  Wang, Shu Lin ^a,b   You, Hong Zhu ^a   Lei, Ying Ke ^a,c   Li, Xue Ling ^a  

^a INSTITUTE OF INTELLIGENT MACHINES   (China)

^b HUNAN UNIVERSITY   (China)

^c ELECTRONIC ENGINEERING INSTITUTE   (China)

Author keywords

dimensionality reduction; Gene expression profiles; linear discriminative analysis; multidimensional scaling; principal component analysis; tumor classification

Indexed keywords

DIMENSIONALITY REDUCTION; GENE EXPRESSION PROFILES; LINEAR DISCRIMINATIVE ANALYSIS; MULTI-DIMENSIONAL SCALING; PRINCIPAL COMPONENTS; TUMOR CLASSIFICATION;

CLASSIFICATION (OF INFORMATION); COMPUTATION THEORY; DATA PROCESSING; DATA REDUCTION; FEATURE EXTRACTION; FORECASTING; GENE EXPRESSION; INTELLIGENT COMPUTING; LEARNING SYSTEMS; MACHINE DESIGN; NUMBER THEORY; TUMORS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77958460554     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14922-1_37     Document Type: Conference Paper

References (19)

1. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., Lander, E. S.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531-537 (1999)
2. Asyali, M. H., Colak, D., Demirkaya, O., Inan, M. S.: Gene expression profile classification: A review. Current Bioinformatics 1, 55-73 (2006)
3. Rocke, D. M., Ideker, T., Troyanskaya, O., Quackenbush, J., Dopazo, J.: Papers on normalization, variable selection, classification or clustering of microarray data. Bioinformatics 25, 701-702 (2009)
4. Wang, S. L., Gui, J., Li, X. L.: Factor analysis for cross-platform tumor classification based on gene expression profiles. Journal of Circuits Systems and Computers 19, 243-258 (2010)
5. Belhumeur, P. N., Hespanha, J. P., Kriegman, D. J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711-720 (1997)
6. Cox, T., Cox, M.: Multidimensional scaling. Chapman & Hall, London (1994)
7. Wang, S. L., Wang, J., Chen, H. W., Zhang, B. Y.: SVM-based tumor classification with gene expression data. In: Li, X., Zaïane, O. R., Li, Z.-h. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 864-870. Springer, Heidelberg (2006)
8. Lu, Y. J., Tian, Q., Sanchez, M., Wang, Y. F.: Hybrid PCA and LDA analysis of microarray gene expression data. In: Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 372-377 (2005)
9. Taguchi, Y., Oono, Y.: Relational patterns of gene expression via non-metric multidimensional scaling analysis. Bioinformatics 21, 730-740 (2005)
10. Chen, Y., Meltzer, P. S.: Gene expression analysis via multidimensional scaling. Curr. Protoc. Bioinformatics, ch. 7, Unit 7.11 (2005)
11. Ye, J. P., Li, T., Xiong, T., Janardan, R.: Using uncorrelated discriminant analysis for tissue classification with gene expression data. IEEE-ACM Transactions on Computational Biology and Bioinformatics 1, 181-190 (2004)
12. Kruskal, W. H., Wallis, W. A.: Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association 47, 583-621 (1952)
13. Kononenko, I.: Estimating attributes: Analysis and extensions of Relieff. In: European Conference on Machine Learning, pp. 171-182. Springer, Catana (1994)
14. Armstrong, S. A., Staunton, J. E., Silverman, L. B., Pieters, R., de Boer, M. L., Minden, M. D., Sallan, S. E., Lander, E. S., Golub, T. R., Korsmeyer, S. J.: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics 30, 41-47 (2002)
15. Nutt, C. L., Mani, D. R., Betensky, R. A., Tamayo, P., Cairncross, J. G., Ladd, C., Pohl, U., Hartmann, C., McLaughlin, M. E., Batchelor, T. T., Black, P. M., von Deimling, A., Pomeroy, S. L., Golub, T. R., Louis, D. N.: Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Research 63, 1602-1607 (2003)
16. Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C. T., Gaasenbeek, M., Angelo, M., Reich, M., Pinkus, G. S., Ray, T. S., Koval, M. A., Last, K. W., Norton, A., Lister, T. A., Mesirov, J., Neuberg, D. S., Lander, E. S., Aster, J. C., Golub, T. R.: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine 8, 68-74 (2002)
17. Yeoh, E. J., Ross, M. E., Shurtleff, S. A., Williams, W. K., Patel, D., Mahfouz, R., Behm, F. G., Raimondi, S. C., Relling, M. V., Patel, A., Cheng, C., Campana, D., Wilkins, D., Zhou, X. D., Li, J. Y., Liu, H. Q., Pui, C. H., Evans, W. E., Naeve, C., Wong, L. S., Downing, J. R.: Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1, 133-143 (2002)
18. Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., Meltzer, P. S.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7, 673-679 (2001)
19. Ambroise, C., McLachlan, G. J.: Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences of the United States of America 99, 6562-6566 (2002)