Reference: | 牛頓雜誌編輯部,「孜孜不倦地實驗,也會找到新發現;訪問日本島津製 作所田中耕一研究員」,牛頓雜誌國際中文版第235期,2003年3月號。 牛頓雜誌編輯部,「我的新挑戰!訪問日本島津製作所田中耕一紀念質量 分析研究所」,牛頓雜誌國際中文版第242期,2003年10月號。 行政院衛生署,「中華民國九十四年台灣地區死因統計結果摘要」。 網址:http://www.doh.gov.tw/statistic/data/死因摘要/94年/94.htm 行政院衛生署,國民健康局,「94年度衛生教育宣導主軸-癌症預防」。 網址:http://www.bhp.doh.gov.tw/BHP/index.jsp 行政院衛生署,「中華民國九十四年臺灣地區主要癌症死亡原因」。 網址:http://www.doh.gov.tw/statistic/data/死因摘要/94年/表8.xls 全國癌症病友服務中心,「攝護腺癌(90.02.01)衛教手冊之十八」。 網址:http://www2.cch.org.tw/OURHOME/booklet/booklet18.htm 徐竣建,「重疊法應用於蛋白質質譜資料」,國立政治大學統計系研究所碩士論文,2006年,指導教授:余清祥博士。 國泰綜合醫院,癌症資訊網,「攝護腺癌症簡介」。 網址:http://www1.cgh.org.tw/content/healthy/cancerx/newpage19.htm 黃仁澤,「對於高維度資料進行特徵選取─應用於分類蛋白質質譜儀資料」,國立政治大學統計系研究所碩士論文,2005年,指導教授:郭訓志博士、薛慧敏博士。 葉勝宗,「使用AUC特徵選取法在蛋白質質譜資料分析之應用」,國立政治大學統計系研究所碩士論文,2006年,指導教授:張源俊博士,郭訓志博士。 陳敏鋑,「認識癌症」,癌症關懷季刊,德桃基金會。 網址:http://med.mc.ntu.edu.tw/~onc/Lecture/cancer1.html 賴基銘,「癌症篩檢未來的展望:SELDI血清蛋白指紋圖譜的應用」,國家 衛生研究院電子報第52期,2004年6月25日。 Adam, B.L., Qu, Y., Davis, J.W., Ward, M.D., Clements, M.A., Cazares, L.H., Semmes, O.J., Schellhammer, P.F., Yasui, Y., Feng, Z. and Wright, G.L. Jr. (2002) “Serum Protein Fingerprinting Coupled with a Pattern- matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men.” Cancer Research, Vol. 62, No. 13, pp. 3609-14. Alpaydin, E. and Kaynak, C. (1998), “Cascading Classifiers.” Kybernetika, Vol. 34, No. 4, pp. 369-374. Alpaydin, E. and Kaynak, C. (2000) “MultiStage Cascading of Multiple Classifiers: One Man’s Noise is Another Man’s Data.” In Seventeenth International Conference on Machine Learning, ed. P. Langley, pp. 455-462. San Francisco: Morgan Kaufmann. Alpaydin, E. (2004), Introduction to Machine Learning, MIT Press. Bryan,J. G. (1951), “The Generalized Discriminant Function: Mathematical Foundations and Computational Routine.” Harvard Educational Review, Vol. 21, pp. 90-95. Breiman, L. (1996) “Bagging Predictor.” Machine Learning, Vol. 24, No. 2, pp.123-140. Burbidge, R., Trotter, M., Buxton, B. F. and Holden, S. B. (2001), “Drug Design by Machine Learning: Support Vector Machine for Pharmaceutical Data Analysis.” Computers and Chemistry, Vol. 26, pp. 5-14. Chang, Y. C. and Lin, S. C. (2004), “Synergy of Logistic Regression and Support Vector Machine in Multiple-Class Classification.” LNCS, Vol. 3177, pp.132-141. Chen, G., Gharib, T. G., Huang, C. C., Thomas, D. G., Shedden, K. A., Taylor, Jeremy M. G., Kardia, Sharon L.R., Misek, D. E., Giordano, T. J., Tannettoni, M. D., Orringer, M.B., Hanash, S. M. and Beer, D. G.. (2002) “Proteomic Analysis of Lung Adenocarcinoma: Identification of a Highly Expressed Set of Proteins in Tumors.” Clinical Cancer Research, Vol. 8, pp. 2298-2305. Draper, N. R. and Smith, H. (1981), Applied Regression Analysis, 2nd Edn. Wiley, New York. Dudani, S. A. (1976) “The distance-weighted k-nearest-neighbor rule.” IEEE Transactions on Systems, Man, and Cybernetics, 6(4):325-327. Fisher, R. A. (1936), “The Use of Multiple Measurements in Taxonomic Problems.” Annals of Eugenics, Vol. 7, pp. 179-188. Fix, E. and Hodges, J. L. (1951), “Discriminatory Analysis-Nonparametric Discrimination: Consistency Properties.” Report No. 4, US Air Force School of Aviation Medicine, Random Field, Texas. [Published in Agrawala (1997), Silverman and Jones (1989) and Dasarathy (1991).] Furey, T., Schummer, M., Duffy, N., Bednarski, D., Haussler, D. and Cristiannini, N. (2000), “Support Vector Machine Classification and Validation of Caner Tissue Samples Using Microarray Expression Data.” Bioinformatics, Vol. 16, pp. 906-914. Guyon, I., Weston, J. and Barnhill, S. “Gene selection for cancer classification using support vector machines.” Machine Learning, 46(1): 389-422 Hastie, T., Tibshirani, R. and Friedman, J. (2001) The Elements of Statistical Learning. Springer. Holland, J.H. (1994) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 3rd edn. Cambridge, MA: MIT Press. Johnson, R. A. and Wichern, D. W. (2002), Applied Multivariate Statistical Analysis, Prentice-Hall, Inc. Upper Saddle River, NJ, USA. Kohonen, Y. (1982) “Self-Organizing Formation of Topologically Correct Feature Maps.” Biological Cybernetics, Vol. 43, pp. 59-69. Kohonen, T. (1990) “The Self-Organizing Map”, Proc Inst Electrical Electronics Eng, Vol. 78, pp. 1464-1480. Lilien, R.H., Farid, H. and Donald, B.R. (2003), “Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum.” Journal of Computational Biology, Vol. 10, No. 6, pp.925-946. Osuna, E., Freund, R. and Girosi, F. (1997), “Training Support Vector Machines: An Application to Face Detection.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 130-136. Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C. and Liotta, L.A. (2002) “Use of Proteomic Patterns in Serum to Identify Ovarian Cancer.” Lancet, Vol. 359, Iss. 9306, pp. 572-577. Qu, Y., Adam, B.L., Thornquist, M., Potter, J.D., Thompson, M.L., Yasui, Y., Davis, J., Schellhammer,P. F., Cazares,L., Clements,M.A., Wright, Jr.G.L. and Feng, Z. (2003), “Data Reduction Using a Discrete Wavelet Transform in Discriminant Analysis of Very High Dimensionality Data.” Biometrics, Vol. 59, pp, 143–151. Rao, C. R. (1948), “The Utilization of Multiple Measurements in Problems of Model Uncertainty in Generalized Linear Models.” Journal of The Royal Statistical Society series B, Vol. 10, pp. 159-203. Ripley, B. D. (1996), Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press. Sauve, A. C. and Speed, T. P. (2004) “ Normalization, Baseline Correction and Alignment of High-Throughput Mass Spectrometry Data.” Proceedings Gensips 2004 Schölkopf, B. Herbrich, R. and Smola, A. J. (2001) “The General Representer Theorem.” LNAI, Vol. 2111, pp. 416-426. Tong, S. and Koller, D. (2002), “Support vector machine active learning with applications to text classification.” The Journal of Machine Learning Research, Vol.2, pp.45-66. Trafalis, T. B. and Ince, H. (2000), “Support Vector Machine for Regression and Application to Financial Forecasting.” Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Vol. 6, pp.6348-6353. Vapnik, V. N. (1995), The Nature of Statistical Learning Theory, Springer, New York. Wolpert, D. H. (1992), “Stacked Generalization.” Neural Networks, Vol.5, pp241-259. Wu, B., Abbott, T., Fishman, D., McMurray W., Mor, G., Stone, K., Ward, D., Williams, K. and Zhao, H. (2003), “Comparison of Statistical Methods for Classification of Ovarian Cancer Using Mass Spectrometry Data.” Bioinformatics, Vol. 19, No. 13, pp. 1636-1643. Zhang, X., Mesirov, J. P. and Waltz, D. L. (1992) “Hybird System for Protein Secondary Structure Prediction.” NCBI, Vol. 255, No.4, pp.1049-1063. Zou, H. and Hastie, T. (2004) “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society, Series B, Vol. 67, pp. 301-320. |