Developmental stage annotation of Drosophila gene expression pattern images via an entire solution path for LDA

ACM Transactions on Knowledge Discovery from Data

Volumn 2, Issue 1, 2008, Pages

  Ye, Jieping ^a,c   Chen, Jianhui ^a,c   Janardan, Ravi ^b,d   Kumar, Sudhir ^a,e  

^a ARIZONA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c Arizona State University   (United States)

^d University of Minnesota   (United States)

^e ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Dimensionality reduction; Gene expression pattern image; Linear discriminant analysis; Linear regression

Indexed keywords

DIMENSIONALITY REDUCTION; GENE EXPRESSION PATTERN IMAGE; LINEAR DISCRIMINANT ANALYSIS;

ALGORITHMS; FEATURE EXTRACTION; GENE EXPRESSION; LINEAR REGRESSION; MATHEMATICAL MODELS; PARAMETER ESTIMATION;

IMAGE ANALYSIS;

EID: 42149163917     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/1342320.1342324     Document Type: Article

References (33)

1. BELHUMEOUR, P., HESPANHA, J., AND KRIEGMAN, D. 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 7, 711-720.
2. BOWNES, M. 1975. A photographic study of development in the living embryo of Drosophila melanogaster. J. Embryol. Exp. Morphol. 33, 789-801.
3. CARROLL, S., GRENIER, J., AND WEATHERBEE, S. 2005. From DNA to Diversity: Molecular Genetics and the Evolution of Animal Design. 2nd Ed. Blackwell Publishing.
4. D'ASPREMONT, A., GHAOUI, L., JORDAN, M., AND LANCKRIET, G. 2004. A direct formulation for sparse PCA using semidefinite programming. In Proceedings of the Conference on Advance in Neural Information Processing Systems. 41-48.
5. DAUGMAN, J. 1988. Complete discrete 2-d gabor transforms by neural networks for image analysis and compression. IEEE Trans. Acoust. Speech Signal Proc. 36, 7, 1169-1179.
6. DONOHO, D. 2006. For most large underdetermined systems of linear equations, the minimal 11-norm near-solution approximates the sparsest near-solution. Comm. Pure Appl. Math. 59, 7, 907-934.
7. DUDA, R., HART, P., AND STORK, D. 2000. Pattern Classification. John Wiley.
8. EFRON, B., HASTIE, T., JOHNSTONE, I., AND TIBSHIRANI, R. 2004. Least angle regression. Annals Statis. 32, 2, 407-499.
9. FISHER, R. 1936. The use of multiple measurements in taxonomie problems. Annals Eugenics 7, 179-188.
10. FUKUNAGA, K. 1990. Introduction to Statistical Pattern Classification. Academic Press.
11. GOLUB, G. H. AND VAN LOAN, C. F. 1996. Matrix Computations. The Johns Hopkins University Press.
12. GOLUB ET AL., T. 1999. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 5439, 531-537.
13. GONZALEZ, R. AND WOODS, R. 1993. Digital Image Processing, 2nd Ed. Addison-Wesley.
14. GUO, Y., HASTIE, T., AND TIBSHIRANI, R. 2003. Regularized discriminant analysis and its application in microarrays. Tech. Rep. Stanford University.
15. GURUNATHAN, R., VAN EMDEN, B., PANCHANATHAN, S., AND KUMAR, S. 2004. Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: Binary feature versus invariant moment digital representations. BMC Bioinform. 5, 1, 202.
16. HARTENSTEIN, V 1993. Atlas of Drosophila Development. Cold Spring Harbor Laboratory Press.
17. HASTIE, T., ROSSET, S., TIBSHIRANI, R., AND ZHU, J. 2004. The entire regularization path for the support vector machine. J. Mach. Learn. Resear. 5, 1391-1415.
18. HASTIE, T., TIBSHIRANI, R., AND FRIEDMAN, J. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
19. IE, E., WESTON, J., NOBLE, W., AND LESLIE, C. 2005. Multi-class protein fold recognition using adaptive codes. In Proceedings of the International Conference on Machine Learning. 329-336.
20. KUMAR, S., JAYARAMAN, K., PANCHANATHAN, S., GURUNATHAN, R., MARTI-SUBIRANA, A., AND NEWFELD, S. 2002. BEST: A novel computational approach for comparing gene expression patterns from early stages of Drosophila melanogaster development. Genetics 162, 4, 2037-2047.
21. LEE, Y., LIN, Y., AND WAHBA, G. 2004. Multicategory Support Vector Machines, theory, and application to the classification of microarray data and satellite radiance data. J. Amer. Statist. Assoc. 99, 67-81.
22. MIKA, S. 2002. Kernel Fisher Discriminants. Ph.D. thesis, University of Technology, Berlin, Germany.
23. PARK, C. AND PARK, H. 2005. A relationship between LDA and the generalized minimum squared error solution. SIAM J. Matrix Anal. Appl. 27, 2, 474-492.
24. PENG, H. AND MYERS, E. 2004. Comparing in situ mRNA expression patterns of Drosophila embryos. In Proceedings of the Conference on Research in Computational Molecular Biology. 157-166.
25. TIBSHIRANI, R. 1996. Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc. B. 1, 267-288.
26. TOMANCAK ET AL., R 2002. Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 3, 12, 1-14.
27. 1-norm multi-class support vector machines: Methodology and theory, http://www.stat.umn.edu/~xshen/paper/msvm_jasa_rev2.pdf.
28. YE, J. 2005. Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J. Mach. Learn. Resear. 6, 483-502.
29. YE, J., CHEN, J., LI, Q., AND KUMAR, S. 2006. Classification of Drosophila embryonic developmental stage range based on gene expression pattern images, In Proceedings of the IEEE Computational Systems Bioinformatics Conference. 293-298.
30. YE, J., JANARDAN, R., AND LI, Q. 2004. Two-dimensional linear discriminant analysis. In Proceedings of the Conference on Advance in Neural Information Processing Systems. 1569-1576.
31. YE, J. AND XIONG, T. 2006. Computational and theoretical analysis of null space and orthogonal linear discriminant analysis. J. Mach. Learn. Res., 1183-1204.
32. ZHANG, R AND RIEDEL, N. 2005. Discriminant analysis: A unified approach. In ICDM. 514-521.
33. ZHU, J., ROSSET, S., HASTIE, T., AND TIBSHIRANI, R. 2003. 1-norm support vector machines. In Proceedings of the Conference on Advance in Neural Information Processing Systems. 49-56.