Please use this identifier to cite or link to this item:
標題: 核心邏輯斯迴歸模式之微陣列資料分析:次序類別的癌症分類
Kernel logistic regression based microarray data analysis: The ordinal scale cancer classification
作者: 陳陵姿
Chen, Ling-Zi
關鍵字: microarray;微陣列;classification;kernel method;logistic regression;ordered categories;分類;核心方法;邏輯斯迴歸;次序類別
出版社: 應用數學系所
引用: 1. Golub, T.R., et al., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 1999. 286(5439): p. 531-7. 2. Hastie, T., R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer series in statistics. 2001, New Your: Springer-Verlag. 3. Simon, R.M., et al., Design and Analysis of DNA Microarray Investigations. 2003, New York: Springer-Verlag. 4. Speed, T., Statistical analysis of gene expression microarray data. Interdisciplinary statistics series, ed. N. Keiding, et al. 2003, New York: Chapman & Hall / crc. 5. Fort, G. and S. Lambert-Lacroix, Classification using partial least squares with penalized logistic regression. Bioinformatics, 2005. 21(7): p. 1104-11. 6. Li, W., F. Sun, and I. Grosse, Extreme value distribution based gene selection criteria for discriminant microarray data analysis using logistic regression. J Comput Biol, 2004. 11(2-3): p. 215-26. 7. Nguyen, D.V. and D.M. Rocke, Multi-class cancer classification via partial least squares with gene expression profiles. Bioinformatics, 2002. 18(9): p. 1216-26. 8. Ananth, C.V. and D.G. Kleinbaum, Regression models for ordinal responses: a review of methods and applications. Int J Epidemiol, 1997. 26(6): p. 1323-33. 9. Hosmer, D.W. and S. Lemeshow, Applied logistic regression. 2000, New Youk: John Wiley and Sons. 10. McCullagh, P., Regression models for ordinal data. Journal of the royal statistical society, 1980. 42(2): p. 109 -142. 11. Evgeniou, T., M. Pontil, and T. Poggio, Regularization Networks and Support Vector Machines. Advances in Computational Mathematics, 2000. 13(1): p. 1-50. 12. Wahba, G., Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics 59. 1990: SIAM. 13. O''Sullivan, F., B.S. Yandell, and W.J.J. Raynor, Automatic Smoothing of Regression Functions in Generalized Linear Models. Journal of the American Statistical Association, 1986. 81(393): p. 96-103. 14. Wahba, G., et al., Soft classification, a. k. a. risk estimation, via penalized log-likelihood and smothing spline analysis of variance. The mathematics of generalization, 1995. 15. Zhu, J. and T. Hastie, Kernel logistic regression and the import vector machine. Journal of Computational & Graphical Statics, American Statistical Association, 2005. 14: p. 185-205. 16. Wahba, G., et al., Smoothing spline ANOVA for exponential families, with application to the Wisconsin Epidemiological Study of Diabetic Retinopathy: the 1994 Neyman Memorial Lecture. The annuals of statistics, 1995. 23(6): p. 1865 -1895. 17. Scholkopf, B., et al., A Generalized Representer Theorem. Lecture Notes In Computer Science; Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory, 2001. 2111: p. 416 - 426. 18. Hogg, R. and E. Tanis, Probability and statistical inference. 6 ed. 2001, New York: Prentice Hall. 19. Benjamini, Y. and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc., 1995(Ser. B 57): p. 1, 289--1,300. 20. Dyrskjot, L., et al., Identifying distinct classes of bladder carcinoma using microarrays. Nat Genet, 2003. 33(1): p. 90-6. 21. Wong, Y.F., et al., Expression genomics of cervical cancer: molecular classification and prediction of radiotherapy response by DNA microarray. Clin Cancer Res, 2003. 9(15): p. 5486-92. 22. Hsuan-Yu Chen, et al., A Five-Gene Signature and Clinical Outcome in Non-Small-Cell Lung Cancer. The New England Journal of Medicine 2007; 356: 11-20
微陣列技術已被廣泛應用在癌症研究上,藉由分析資料列陣中的基因表現,進階找出癌症的分子變異區別。在本文裡,藉由微陣列資料分析於多級癌症分類的技巧,對比現況多數的分類規程與建立,皆無考慮類別結構,故我們提出一個新方法,藉由核心技術來推展成正比例勝算邏輯斯迴歸模型於分類例子用於次序性的類別 (例如:癌症階段或等級)。最後,以模擬方式和公開的微陣列資料集來比較分類新方法的優劣。

Microarray has demonstrated useful applications in cancer research. By analyzing the array generated gene expression data, cancers are distinguished by their molecular variations. In this paper, the multiclass cancer classification by using microarray data is addressed. In contrast to most existing classification procedures established without considering the class structure, we propose a new method by applying the kernel technique to generalize the proportional odds logistic regression for categorizing examples into ordered classes (e.g., cancer stages or grades). The performance of resulting classifier is demonstrated on simulated and publicly available microarray datasets.
其他識別: U0005-2506200722205400
Appears in Collections:應用數學系所

Show full item record
TAIR Related Article

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.