Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis

Expert Systems with Applications

Volumn 36, Issue 4, 2009, Pages 7644-7650

  Li, Xuehua ^a   Shu, Lan ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Dimensionality reduction; Genomic microarray; Kernel methods; Locally linear embedding; Support vector machine

Indexed keywords

BIOACTIVITY; BIOINFORMATICS; DATA REDUCTION; FEATURE EXTRACTION; GENE EXPRESSION; IMAGE RETRIEVAL; LEARNING SYSTEMS; MICROARRAYS; NUCLEIC ACIDS; ORGANIC ACIDS; SUPPORT VECTOR MACHINES;

DIMENSIONALITY REDUCTION; GENOMIC MICROARRAY; KERNEL METHODS; LOCALLY LINEAR EMBEDDING; SUPPORT VECTOR MACHINE;

BIOASSAY;

EID: 60249097470     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.09.070     Document Type: Article

References (35)

1. Alizadeh A.A., Eisen M.B., et al. Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403 (2000) 503-511
2. Belkin M., and Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15 6 (2003) 1373-1396
3. Brown M.P., Grundy W.N., Lin D., Cristianini N., Sugnet C.W., Furey T.S., et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences 97 (2000) 262-267
4. Elgammal A.M., and Lee C.S. Separating style and content on a nonlinear manifold. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2004) 478-485
5. Ellis M., Davis N., Coop A., Liu M., Schumaker L., Lee R.Y., et al. Development and validation of a method for using breast core needle biopsies for gene expression microarray analyses. Clinical Cancer Research 8 5 (2002) 1155-1166
6. Haykin S. Neural networks: A comprehensive foundation (1999), Practice-Hall Press, New Jersey (pp. 330-332)
7. Hinton G., and Roweis S. Stochastic neighbor embedding. Proceedings of neural information systems: Natural and synthetic (2003), MIT press, Cambridge (pp. 833-840)
8. Jolliffe I.T. Principal component analysis (1986), Springer, New York
9. Keller J.M., Gray M.R., and Givens J.A. A fuzzy k-nearest neighbours algorithm. IEEE Transactions on Systems, Man, and Cybernetics 15 (1985) 580-585
10. Khan J., Wei J.S., Ringner M., Saal L.H., Ladanyi M., Westermann F., et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7 6 (2001) 673-679
11. Kouropteva, O., Okun, O., & Pietikainen, M. (2002). Selection of the optimal parameter value for the locally linear embedding algorithm, In Proceedings of the 1st international conference on fuzzy systems and knowledge discovery, Singapore (pp. 359-363).
12. Lee Y., and Lee C.K. Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 19 9 (2003) 1132-1139
13. Lee J.A., and Verleysen M. Nonlinear dimensionality reduction of data manifolds with essential loops. Neurocomputing 67 (2005) 29-53
14. Marina, M., & Shi, J. b. (2001). Learning segmentation by random walks, Advances in NIPS 13, Cambridge (pp. 873-879).
15. Mekuz, N., Bauckhage, C., & Tsotsos, J. K. (2005). Face recognition with weighted locally linear embedding, In The second Canadian conference on computer and robot vision (pp. 290-296).
16. Nikhil R.P., and Kripamoy A. Animesh Sharma and Shun-ichi Amari: Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering. BMC Bioinformatics 8 (2007) 5
17. Orr M.S., and Scherf U. Large-scale gene expression analysis in molecular target discovery. Leukemia 16 4 (2002) 473-477
18. Roweis S.T., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000) 2323-2326
19. Saul, L., & Roweis, S. (2002). Think globally, fit locally: Unsupervised learning of nonlinear manifolds. Technical report MS CIS-02-18, University of Pennsylvania, 37 (pp. 134-135).
20. Schölkopf B., and Smola A.J. Learning with kernels (2002), MIT Press, Cambridge, MA
21. Schölkopf B., Smola A., and Müller K.R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10 5 (1998) 1299-1319
22. Shalon D., Smith S.J., and Brown P.O. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Research 6 7 (1996) 639-645
23. Shawe T.J., and Cristianini N. Kernel methods for pattern analysis (2004), Cambridge University Press, Cambridge
24. Su C.T., and Yang C.H. Feature selection for the SVM: An application to hypertension diagnosis. Expert Systems with Applications 34 (2008) 754-763
25. Tenenbaum J.B., Silva V.D., and Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 260 (2000) 2319-2323
26. Tibshirani R., Hastie T., Narasimhan B., and Chu G. Class prediction by nearest shrunken centroids with applications to DNA microarrays. Statistical Science 18 (2003) 104-117
27. Troyanskaya O., Cantor M., et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17 (2001) 520-525
28. Tusher V.G., Tibshirani R., and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Science, USA 98 (2001) 5116-5121
29. Vapnik V.N. The nature of statistical learning theory (1995), Springer-Verlag, NY
30. Vapnik V.N. Statistical learning theory (1998), John Wiley, New York (pp. 157-169)
31. Wang X.C., and Paliwal K.K. Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition, Pergamon. The Journal of the Pattern Recognition Society 36 (2003) 2429-2439
32. Yeo, G., & Poggio, T. (2001). Multiclass classification of SRBCTs, Technical report AI Memo 2001-018 CBCL Memo 206, MIT.
33. Young R.A. Biomedical discovery with DNA arrays. Cell 102 (2000) 9-15
34. Zhang C., Wang J., Zhao N., and Zhang D. Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction. Pattern Recognition 37 2 (2004) 325-336
35. Zhang Z., and Zha H. Principal manifolds and nonlinear dimension reduction via local tangent space alignment. SIAM Journal of Scientific Computing 26 (2004) 313-338