Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/17673
標題: 以邏輯斯迴歸模式探討在微陣列資料上的多類別癌症分類
Multi-class Cancer Classification on Microarray Data by Logistic Regression Models
作者: 吳怡萱
吳怡萱, Yi-Xuan Wu
關鍵字: microarray;微陣列矩陣;multi-class classifier;logistic discrimination;misclassification;多重類別分類器;邏輯斯分群;誤歸率
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. Li, T., C. Zhang, and M. Ogihara, A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 2004. 20(15): p. 2429-37. 3. Wu, B., Differential gene expression detection and sample classification using penalized linear regression models. Bioinformatics, 2005. 4. 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. 5. Simon, R.M., et al., Design and Analysis of DNA Microarray Investigations. 2003, New York: Springer-Verlag. 6. Speed, T., statistical analysis of gene expression microarray data. interdisciplinary statistics series, ed. b.m. n.keiding, t. speed, p.van der heijden. 2003, new york: chapman & hall/crc. 7. Fort, G. and S. Lambert-Lacroix, Classification using partial least squares with penalized logistic regression. Bioinformatics, 2005. 21(7): p. 1104-11. 8. 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. 9. 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. 10. hosmer, d.w.l., stanley, applied logistic regression. 2000. 11. Casella, G. and R. Berger, Statistical Inference. 2002, Pacific Grove CA: Duxbury. 12. 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. 13. ripley, b.d., pattern recognition and neural networks. 1996: cambridge university press. 14. Levenberg, K., A Method for the Solution of Certain Problems in Least Squares. Quart. Appl. Math., 1944. 2: p. 164-168. 15. Marquardt, D., An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM J. Appl. Math., 1963. 11: p. 431-441. 16. Dyrskjot, L., et al., Identifying distinct classes of bladder carcinoma using microarrays. Nat Genet, 2003. 33(1): p. 90-6. 17. Cromer, A., et al., Identification of genes associated with tumorigenesis and metastatic potential of hypopharyngeal cancer by microarray analysis. Oncogene, 2004. 23(14): p. 2484-98. 18. 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. 19. Dehan and K. N, Non Small Cell Lung Cancer. 2005.
摘要: 
動機:
微陣列被使用在研究癌症的課題上的情形與日俱增。藉由微陣列能同時偵測數千個基因的表現量,進而能區別不同分子變異的癌症,也能更精確地將癌症分類。當統計方法已被廣泛地用在兩群分類的評估上,但是對於多類別的癌症分群只有極少數的報告。因此我們想要對多類別的癌症作探討,希望可以區別出不同病人的癌症期別或是期罹患癌症的亞型,幫助醫療人員在臨床診斷上能更精確有效。

目標:
在此研究中,我們提出以名目尺度和順序尺度作為樣本分類的微陣列資料,應用基本邏輯斯區別分析( Logistic Discrimination (LD)) 方法來進行多重類別癌症的分類,例如,不同組織樣本的癌症亞型和癌症期別。以LD為基礎的分類器和以常態模型分群法為基礎的分類器作比較來分析微陣列資料,由誤歸率的評估來衡量其成效的好壞。

Motivation
Microarray has been increasingly used in cancer research. Using expression levels of thousands of genes monitored simultaneously by microarray, tumors' molecular variations are distinguished, and cancers are more accurately classified. While statistical methods have been extensively evaluated for dichotomous classifications, there are only limited reports on the important issue of multi-class cancer classification. It needs to explore the statistical methods of the multi-class cancer classification.

Objective
In this research, we address multi-class cancer classifications by applying logistic discrimination (LD) based methods on microarray data of nominal and ordinal scaled sample class outcomes, e.g., tissue samples of different cancer subtypes and cancer stages. LD based classifiers are assessed by misclassification rates on microarray data and comparing with normal model discrimination based classifiers.
URI: http://hdl.handle.net/11455/17673
其他識別: U0005-2806200616292900
Appears in Collections:應用數學系所

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