臺灣博碩士論文加值系統

癌症，可謂是人類在邁向二十一世紀中最大的夢魘，尤其位於國人十大死亡原因之首；民國71年開始，癌症躍居十大死亡原因榜首，迄今仍居高不下。成長速度之快，已遠遠超脫人類所能掌握與預防的範疇。有鑒於此，生物醫學領域中的科學家們也從以往被動的角色轉變成為主動的探索者，期望能藉由過往所獲得的各項珍貴病理資料與近期所發表的人類基因圖譜解碼計畫相互結合下，在人類這龐大的基因迷宮中尋求癌細胞生成的原因與規則。本文中所探討的卵巢癌是常見的婦科癌症之一，它也跟多數的癌症一樣，發生原因與條件迄今仍未明朗。因此，期望藉由本研究中所設計的研究方法；透過對微陣列晶片於卵巢癌病患中所獲得的各項基因資料在智慧型計算與決策樹系統的混合應用下，能有效且準確的判別及篩選出具關鍵性的特徵基因。醫生也可藉由這些具關鍵的特徵基因替病患判斷患病的可信度，確認患病後處置的判斷依據，並依據這些病理資料達到癌症有效的預防。

The Cancers may be one of the nightmares of the humanity in advances into 22 century. Furthermore, the increase speed of cancer has surpassed the scope which the humanity can understand. Taking this into consideration, scientists have transformed passive role into the steer seeker in the domain of biomedicine. Because of precious pathology material and the mutually union project of Human Genome, it is possible to find the production reason of cancer cell and the rule in the humanity's huge gene labyrinth. Without definite reasons, the ovarian cancer is one of common gynecology cancers. In this paper, the computation intelligence with decision tree can effectively get the target genes. Due to obtained target genes, doctors can effectively use these pathology data to achieve the cancer effective prevention.

誌謝....................I
摘要....................II
ABSTRACT...............III
目錄....................IV
表錄....................V
圖錄....................VI
一、緒論.................1
1.1.研究背景與動機........1
1.2.研究目的.............2
1.3.研究限制與研究範圍.... 4
二、文獻探討............. 5
2.1.決策樹.............. 8
2.2.Boost.............. 11
2.3.粒子群演算法......... 13
三、研究內容............. 17
3.1.研究資料............ 17
3.2.研究設計............ 17
四、實驗成果與分析 ........20
五、結論與未來研究方向.....29
參考文獻.................30
附錄一.................. 33
附錄二.................. 43

[1]陳健尉，「生物晶片之生技應用」，台灣生物科技產學論壇，國立中興大學，台中市，民國九十二年。
[2]陳健尉，「生物晶片在生物醫學上之應用」，生物科技新知研習會，國立中興大學，台中市，民國九十三年八月。
[3]盧信智，「應用卵巢癌之微陣列晶片分析基因表現量及調控路徑」，國立台灣大學電機工程系碩士論文，民國九十五年六月。
[4]莊麗月，楊正宏，杜崇睿，張學偉，「GA-PSO用於基因表現資料的特徵選取」，2006數位科技與創新管理國際研討會，華梵大學，台北縣，民國九十五年六月。
[5]黃彥博，「智慧型混合系統應用於卵巢癌之微陣列基因篩選與分類 」，華梵大學資訊管理系碩士論文，民國九十六年六月。
[6]郭俊利，「針對生物微晶片資料利用決策樹選取關鍵基因 」，國立中央大學資訊工程研究碩士論文，民國九十五年六月。
[7]盧信智，「應用卵巢癌之微陣列晶片分析基因表現量及調控路徑」，國立台灣大學電機工程系碩士論文，民國九十五年六月。
[8]李維平、江正文，運用活化策略改良粒子群演算法求解成效，先進工程學刊，第八卷第四期，第283-293頁，民國九十六年十月。
[9]許盛家、林詩偉、陳士杰，「應用粒子群演算法為基礎之決策樹演算法於人員績效分類預測-以某科技公司為例」，2008系統科學與工程會議，2008年6月6-7日，宜蘭大學，(2008) .
[10]Kieff, F. Scott、Olin, John M., “Perspectives on Properties of the Human Genome Project ”, Lightning Source Inc, U.S.A, 2003, Chapter 1.
[11]Fodor S. P. A., “Massively parallel genomics”, Science Vol. 277, 1997, pp. 393-395.
[12]Fodor S. P. A., Read J. L., Pirrung M.C., Stryer L., Lu A. T. and Solas D., “Light-directed, spatially addressable parallel chemical synthesis”, Science Vol. 251, 1991, pp. 767-773.
[13]Fodor S. P. A., Rava R. P., Huang X. C., Pease A. C., Holmes C. P. and Adams C. L., “Multiplexed biochemical assays with biological chips”, Nature Vol. 364, 1993, pp. 555-556.
[14]Derisi J., Penland L., Brown P. O., Bittner M. L., Meltzer PS, Ray M., Chen Y., Su Y. A., and Trent J. M., “Use of a cDNA microarray to analyze gene expression patterns in human cancer”, Nature Genetics Vol. 14, 1996, pp. 457-460.
[15]Derisi J. L., Iyer V. R. and Brown P. O., “Exploring the metabolic and genetic control of gene expression on a genomic scale”, Science Vol. 278, 1997, pp. 680-686.
[16]Schena M., Shalon D., Davis R. W. and Brown P. O. “Quantitative monitoring of gene expression patterns with a complementary DNA microarray”, Science Vol. 270, 1995, pp. 467-470.
[17]Velculescu V. E., Z. L., Vogelstein B. and Kinzler K. W. “Serial analysis of gene expression”, Science Vol. 270, 1995, pp. 484-487.
[18]Zhang L., Zhou W., Velculescu V. E., Kern S. E., Hruban R. H., Hamilton S. R., Vogelstein B. and Kinzler K. W. “Gene expression profiles in normal and cancer cells”, Science Vol. 276, 1997, pp. 1268-1272.
[19]Quinlan, J. Ross, “C4.5: Programs For Machine Learning”, Morgan, San Mateo, CA, 1993.
[20]Schapire, R.E.,“The strength of weak learnability”, Machine Learning, Vol. 5, No. 2, 1990, pp. 197-227.
[21]Freund, Y. , “Boosting a weak learning algorithm by majority”, Information and Computation Vol. 121, 1995, No. 2, pp. 256-285.
[22]Freund, Y. and Robert E. Schapire.,“A Decision-Theoretic Generalization of On-
Line Learning and an Application to Boosting”, Journal of Computer and System Sciences Vol. 55, No.1, 1997, pp. 119-139.
[23]Eberhart, R. C., and Kennedy, J.,“A New Optimizer Using Particle Swarm Theory”, Proc. Sixth International Symposium on Micro Machine and Human Science, Japan, 1995, pp. 39-43.
[24] Kennedy, J., and Eberhart, R. C., “Particle Swarm Optimization”, Proc. IEEE International Conf. Neural Networks Vol. 4, 1995, pp. 1942-1948.
[25]Eberhart, R. C., and Shi, Y.,“Particle Swarm Optimization: Developments, Applications and Resources”, Proc. IEEE Int. Conf. On Evolutionary Computation Vol. 1, 2001, pp. 81-86.
[26]Bertucci F., Bernard K., Loriod B., Chang Y. C., Granjeaud S., Birnbaum D., Nguyen C., Peck K. and Jordan B. R.: Sensitivity issues in DNA array-based expression measurements and performance of nylon microarrays for small samples. Human Molecular Genetics Vol. 8, 1999, pp. 1715-1722.
[27]Kennedy, J., and Eberhart, R. C.,“Empirical Study of Particle Swarm Optimization”, Proceedings of the 1999 Congress on Evolutinary Computation Vol. 3, 1999.