單一核甘酸多型性與子宮頸癌癌化因素之分析

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：46

、訪客IP：3.133.159.125

姓名

胡凱智(Kai-Chih Hu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

單一核甘酸多型性與子宮頸癌癌化因素之分析
(Using a Machine Learning Approach toDerive Single Nucleotide Polymorphisms and Microsatellite Cofactors of Cervical Cancer)

相關論文

★ 應用嵌入式系統於呼吸肌肉群訓練儀之系統開發	★ 勃起障礙與缺血性心臟病的雙向研究: 以台灣全人口基礎的世代研究
★ 基質輔助雷射脫附飛行時間式串聯質譜儀微生物抗藥性資料視覺化工具	★ 使用穿戴式裝置分析心律變異及偵測心律不整之應用程式
★ 建立一個自動化分析系統用來分析任何兩種疾病之間的關聯性透過世代研究設計以及使用承保抽樣歸人檔	★ 青光眼病患併發糖尿病,使用Metformin及Sulfonylurea治療得到中風之風險:以台灣人口為基礎的觀察性研究
★ 利用組成識別和序列及空間特性構成之預測系統來針對蛋白質交互作用上的特殊區段點位進行分析及預測辨識	★ 新聞語意特徵擷取流程設計與股價變化關聯性分析
★ 藥物與疾病關聯性自動化分析平台設計與實作	★ 建立財務報告自動分析系統進行股價預測
★ 建立一個分析疾病與癌症關聯性的自動化系統	★ 基於慣性感測器虛擬鍵盤之設計與實作
★ 一個醫療照護監測系統之實作	★ 應用手機開發手握球握力及相關資料之量測
★ 利用關聯分析全面性的搜索癌症關聯疾病	★ 全面性尋找類風濕性關節炎之關聯疾病

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

子宮頸癌是台灣以及全世界女性中主要癌症之一。近十幾年來的研究，某些人類乳突狀病毒已經被確定子宮頸癌的主要造成原因。雖然乳突狀病毒為子宮頸癌發生所必需，但是卻非充分的條件。很顯然有其他環境上的因素以及宿主反應在子宮頸上皮癌化的過程之中也扮演著重要的角色。人類基因體定序的完成以及家族血統相關研究也指出子宮頸癌中基因上的變異性擁有決定性的因素。因此，本研究將單一核甘酸多型性加入分析，以找出哪些基因性因素以及人類乳突狀病毒的組合與子宮頸癌的引發過程中有關。本研究將分析11個單一核甘酸多型性標記，4個microsatellites，年齡與人類乳突狀病毒，使用J48，PART，Id3和PRISM等決策樹分析演算法來分析。同時也比較了這些決策樹演算法的優劣。結果提供我們一個概念，就是我們的確可以將單一核甘酸多型性加入子宮頸癌因素分析，讓我們經由這些因子來預測並分析結果提供醫療用途。

摘要(英)

Cervical cancer is a common cancer among women worldwide. Recently, infection with high-risk types of human papillomavirus (HPV) has been identified as the central cause of cervical cancer. Although HPV has been identified as playing the central role in cervical cancer, it is still insufficient to confer the cervical cancer. Obviously, there are other environmental and host factors which are involved in the progression of HPV infection to cancer. Recent population based twins and family studies have showed the hereditary component of cervical cancer, indicating genetic susceptibility plays an important role. Thus, to take some SNP markers and microsatellite into account as the genetic factors might be helpful to find out some combinations of these genetic factors and HPV that are involved in the progress of precancerous changes into cervical cancer. We take patients’ age, 11 SNP markers and 4 microsatellites into account and perform decision tree analysis using different learning algorithms. We also compare the performance among these learning algorithms. We anticipate that the results of this study will open the door for investigations of identifying the combinations of genetic factors such as SNPs and microsatellites that interact in a non-additive or nonlinear manner to influence risk of common complex multifactorial disease.

關鍵字(中)

★ 微衛星重複性序列
★ 單一核甘酸多型性
★ 子宮頸癌
★ 決策樹
★ 貝式網路

關鍵字(英)

★ microsatellite
★ Bayesian network
★ decision tree
★ Cervical cancer
★ single nucleotide polymorphism

論文目次

Abstract ii
List of Figures iii
List of Tables iv
List of Tables iv
Chapter 1 Introduction 1
Chapter 2 Related Works 3
2.1 The Genome 3
2.2 SNPs 3
2.3 Microsatellites 3
2.4 dbSNP 4
Chapter 3 Materials and Methods 7
3.1 Materials 7
3.2 Methods 10
3.2.1 Data browser and converter 10
3.2.2 Data splitter 13
3.2.3 Plan editor 14
3.2.4 Bayesian Belief Network Analysis 16
3.2.5 Decision Tree Analysis 19
Chapter 4 Results 21
Chapter 5 Discussion 31
References 33
Appendix A 35
A.1 Partial dataset used in our study 35
Appendix B 36
B1. A comparison of different algorithms on the hybrid dataset 36
B2. A comparison of different algorithms on SNPs dataset 37
B3. A comparison of different algorithms on microsatellites dataset 38
Appendix C- Decision Tree 39
C1. Decision tree for the hybrid dataset 39
C2. Decision tree for the SNPs dataset 40
C3. Decision tree for the microsatellite dataset 41
Appendix D 42
D1. The result of Bayesian network for the SNPs dataset 42
D2. The result of Bayesian network for the microsatellites dataset 43

參考文獻

1. Hung-Cheng Lai, Huey-Kang Sytwu, Chien-An Sun, “Single Nucleotide Polymorphism at Fas Promoter is Associated with Cervical Carcinogenesis”, International Journal of Cancer, 103, pp.221-225, 2003.
2. Ross Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufmann Publishers, San Mateo, CA, 1993.
3. Eibe Frank and Ian H. Witten, “Generating Accurate Rule Sets Without Global Optimization” In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers, San Francisco, CA, 1998.
4. R. Quinlan, “Induction of decision trees”, Machine Learning. Vol.1, No.1, pp. 81-106, 1986.
5. J. Cendrowska, “PRISM: An algorithm for inducing modular rules”, International Journal of Man-Machine Studies. Vol.27, No.4, pp.349-370, 1987.
6. T.A. Brown, “GENOMES”, John Wiley & Sons (ASIA) Pte Ltd.
7. Jiawei Han and Micheline Kamber, “Data Mining – Concepts and Techniques”, Morgan Kaufmann Publishers 2001.
8. Wang, Y. and Witten, I.H., “Induction of model trees for predicting continuous classes”. Proceedings of the poster papers of the European Conference on Machine Learning, 1997.
9. Van Rijsbergen, C. J., “Information Retrieval. 2nd edition”, London, Butterworths, 1979.
10. Alvan R. Feinstein, “Clinical Biostatistics”, C.V. Mosby Company, St.Louis, 1977
11. Vasileios Hatzivassiloglou, Pablo A. Duboue, and Andrey Rzhetsky, “Disambiguating Proteins, Genes, and RNA in Text: A Machine Learning Approach”, Bioinformatics, Vol.1, No.1, pp. 1-10, 2001
12. S.T. Sherry, M.H. Ward, M. Kholodov, J. Baker, L. Phan, E.M. Smigielski and K. Sirotkin, “dbSNP: the NCBI Database of Genetic Variation”, Nucleic Acids Research, Vol. 29, No. 1, 2001.
13. Stephen T. Sherry, Minghong Ward, and Karl Sirotkin, “dbSNP—Database for Single Nucleotide Polymorphisms and Other Classes of Minor Genetic Variation”, Genome Research, Vol. 9, Issue 8, 677-679, August 1999.
14. G. Gibson, “Epistasis and Pleiotropy as Natural Properties of Transcriptional Regulation”, Theoretical Population Biology, 49, p58, 1996.
15. Nardone G, “Molecular basis of gastric carcinogenesis”, Aliment Pharmacol Ther. Jun;17 Suppl 2:75-81, 2003.

指導教授

洪炯宗(Jorng-Tzong Horng)

審核日期

2003-7-2

推文