了解基因間調節反應，可以幫助生物學家應用在許多重要的藥物研究上，包括了藥物目標的檢証與藥物的發展。但是，目前基因間的相互調節關係尚未被完全了解出來。雖然生物晶片的技術可以用來量測大量的基因表現，但是它也含有因為雜訊所造成的消失的大量基因表現量。如何從生物晶片資料中推導出正確的基因調節網路對於生物學家來說是一個很大的挑戰。
在本論文中，我們提供一個流程—生物晶片推導基因調節網路基於轉錄因子分析與條件獨立。建立基因調節網路的系統包含三個主要的元件：(一)資物晶片資料前處理步驟、(二)生物知識處理步驟、(三)修改與推導步驟。我們利用貝氏定理中的d-separate準則和條件獨立的觀念來重建基因調節網路。之後，我們使用一些法則來推導在基因調節網路中的反應(增進或是抑制)連線，系統也結合網路上的生物資料庫與工具來自動擷取脫氧核糖核酸(DNA)序列的啟動子去預測能夠調節基因表現的轉錄因子。我們可以視覺性的呈現重建好基因調節網路好讓生物晶片的分析結果可以容易理解。
我們從史丹佛大學所公開的生物晶片資料庫分析了兩種不同的生物晶片資料。我們用兩個策略去評估結果：(一)網路拓撲方法與(二)文獻驗證方法。最後的評估結果顯示我們所重新建立出的基因調節網路可以偵測到可能的癌症基因。

Understanding of gene regulation provides biologists applied in many important drug study including drug target identification and drug development. However, gene regulatory relationships are not yet well understood now. Although Microarray technology provides a large-scale measurement of gene expressions, it also contains large missing gene expressions because of noisy. How to infer correct gene regulatory networks from microarray data is a big challenge to biologists.
In this thesis, we present a workflow for inferring gene regulatory networks from micorarray data based on transcription factor analysis and the conditional independence. The system that constructs gene regulatory networks consists of three components: (I) Microarray data preprocessing, (II) Biological knowledge processing, and (III) Revising and inferring process. We reconstruct the gene regulatory networks using d-separate criteria and conditional independency in Bayesian Network. And then, we use rules to infer interactions (activation or inhibition) of links in the gene regulatory network. The system also integrates the bioinformatics toolkits and databases to automatically extracts the promoter regions of DNA sequences to predict the transcription factors that regulate the gene expressions. We visualize the reconstructed gene regulatory network that the analyzed microarray results are easy to understand.
We analyze two microarray datasets from Stanford Microarray Database. We use two approaches to evaluate our result: (1) the topology of network and (2) verifications based on literature reports. The evaluated results show that the gene regulatory networks we reconstructed could support the process of detecting possible cancer genes.

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