Abstract
DNA microarray allows the measurement of transcript abundances for thousands of genes in parallel. Though, it is an important procedure to select informative genes related to tumor from those gene expression profiles (GEP) because of its characteristics such as high dimensionality, small sample set and many noises. In this paper we proposed a novel method for feature extraction that is named as Orthogonal Discriminant Projection (ODP). This method is a linear approximation base on manifold learning approach. The ODP method characterizes the local and non-local information of manifold distributed data and explores an optimum subspace which can maximize the difference between non-local scatter and the local scatter. Moreover, it introduces the class information to enhance the recognition ability. A trick has been employed to handle the Small Sample Site (SSS). Experimental results on Non-small Cell Lung Cancer (NSCLC) and glioma dataset validates its efficiency compared to other widely used dimensionality reduction methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Genes and Disease. NIH: National Center for Biotechnology Information. NCBI (2008)
Smith, D.G., Ebrahim, S.: Mendelian randomization: Can Genetic Epidemiology Contribute to Understanding Environmental Determinants of Disease? International Journal of Epidemiology 32, 1–22 (2003)
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays. Proc. Natl. Acad. Sci. USA 96, 6745–6750 (1999)
Furey, T.S., Cristianini, N., Duffy, N., David, W., Bednarski, Schummer, M., Haussler, D.: Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. Bioinformatics 16, 906–914
Antoniadis, A., Lambert-Lacroix, S., Leblanc, F.: Effective Dimension Reduction Methods for Tumor Classification Using Gene Expression Data. Bioinformatics 19, 563–570
Huang, D.S., Zheng, C.H.: Independent Component Analysis Based Penalized Discriminant Method for Tumor Classification Using Gene Expression Data. Bioinformatics 22(15), 1855–1862 (2006)
Liao, J.G., Chin, K.V.: Logistic Regression for Disease Classification Using Microarray Data: Model Selection in a Large p and small n case. Bioinformatics 23(15), 1945–1951 (2007)
Nguyen, D.V., Rocke, D.M.: Tumor Classification by Partial Least Squares Using Microarray Gene Expression Data. Bioinformatics 18(1), 39–50 (2002)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Comput. 15(6), 1373–1396 (2003)
He, X., Yang, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face Recognition Using Laplacianfaces. IEEE Trans. Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)
Zhao, H., Sun, S., Jing, Z., Yang, J.: Local Structure Based Supervised Feature Extraction. Pattern Recognition 39, 1546–1550 (2006)
Yang, J., Zhang, D., Yang, J.Y., Niu, B.: Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics. IEEE Trans. Pattern Analysis and Machine Intelligence 29(4), 650–664 (2007)
Niyogi, X.H.P.: Locality Preserving Projections. Advances in Neural Information Processing Systems Vancouver, Canada Univ. of Chicago, Computer Science Department (2002)
Yu, H.C., Bennamoun, M.: 1D-PCA, 2D-PCA to nD-PCA. In: ICPR 2006, 18th International Conference on Pattern Recognition, vol. 4, pp. 181–184 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, C., Li, B. (2008). A New Orthogonal Discriminant Projection Based Prediction Method for Bioinformatic Data. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_126
Download citation
DOI: https://doi.org/10.1007/978-3-540-85984-0_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
eBook Packages: Computer ScienceComputer Science (R0)