臺灣博碩士論文加值系統

在生物體中一切的反應都是和蛋白質、酵素有關係，
而蛋白質、酵素的產生，絕大多數都是和基因之調控
關性有關連，所以找出基因和基因之間的調控關係是
很重要的。我們如果可以找出基因之間的互動網路，
這樣一來就有辦法用基因藥物療法來控制基因表現，
近而控制某些疾病的生長。本論文中提出一套流程，
來分析微陣列實驗產生基因表現的資料，使用幾種群
聚方法來分類基因表現、量測基因之間功能性相關
度，利用以上所提到的功能相關性資料，來建構基因
調控網路。也可以比較在不同的微擾實驗中，有多少
基因的反應路徑是相同的。此外，我們發展一套生物
資訊分析軟體，使用者輸入基因表現的資料，經過分
析，產生在三維空間中的基因調控網路。使用者如果
有特定的目標基因，我們也可以找出有相關性之反應
路徑，讓使用者了解此基因的影響過程。

All biological reaction in the organism relative to protein and enzyme. The production of protein and enzyme relative to gene expression and gene regulation. Finding the regulation of gene is very important. If we find the network of the gene regulation, we will use some medicine to control regulation of the genes or diseases. In this paper we proposed a procedure to analysis gene expression data from microarray experiment. We use some clustering methods to cluster gene expression data and measure the relation of gene-to-gene, if we have the relation of gene-to-gene, we can use the information to reconstruct the gene regulatory network and also we can find how many gene regulatory pathway is the same in many environment. Furthermore, we develop a software to analysis gene expression data. We can generate 3-D gene regulatory network after analysis the gene expression data. Users can use our software to find gene regulatory pathway.

第一章 緒論 1
1.1 生物資訊的起源 1
1.2 生物晶片 3
1.3 基因調控網路(Gene Regulatory Network) 4
1.4 研究動機 5
1.5 論文目的與貢獻 6
1.6 論文架構 7
第二章 文獻探討 9
2.1 群聚分類法 9
2.1.1 階層式分類(Hierarchical clustering) 9
2.1.2 自我組織圖分類(Self-Organizing Map) 11
2.1.3 K-Mean 分群 12
2.1.4 最近鄰居分群 (K-Nearest Neighbor) 13
2.1.5 閥值分群 (Thresholding Clustering) 13
2.1.6 模糊基因分現資料分析 13
2.2 基因調控網路視覺化建立 15
2.2.1 二維基因調控網路 17
2.2.2 三維基因調控網路 18
第三章 研究方法 19
3.1 整體流程 19
3.2 分析資料 19
3.3 群聚方法之使用和基因距離計算方法 20
3.4 基因表現之關連性和調控網路之連結 23
3.4.1 使用階層式分類建立關連性 23
3.4.2 使用自我組織圖建立關連性 25
3.4.3 使用閥值建立關連性 26
3.4.4 使用模糊方法分類 27
3.5 網路之視覺化 28
第四章 研究結果 30
4.1 系統介紹 30
4.1.1 主要功能 30
4.1.2 調控網路視覺化使用 31
4.2 Case Study 32
4.3 驗證結果 33
第五章 結論與未來發展 40
5.1 結論 40
5.2 未來發展 40

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