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研究生:陳德銘
研究生(外文):Te-Ming Chen
論文名稱:利用關聯圖及其貝式網路的近似方法建立切割訊號的模型
論文名稱(外文):Modeling Splice Sites with Dependency Graphs and Their Approximation by Bayesian Networks
指導教授:呂忠津
指導教授(外文):Chung-Chin Lu
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:38
中文關鍵詞:剪接位置關聯圖貝式網路基因識別
外文關鍵詞:splice sitesdependency graphBayesian networkgene identification
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由於生化技術的進步,以及人類基因組計畫的即將完成,大量的去氧核醣核酸(DNA)和蛋白質序列已經被定序。在生物資訊(Bioinformatics)這個新領域中一個相當重要的課題,便是要精準地找出在人類基因組中基因的表現子及介入子(exon-intron)的邊界,通常稱作基因識別(Gene Identification)。同時在基因上分佈了許多訊號可用來找到基因的位置,而這篇論文便是要偵測出其中最重要的訊號稱為剪接位置(splice sites)。
最近用來偵測剪接訊號的方法是利用貝式網路(Bayesian Networks)來建立模型,貝式網路是用來描述有方向性的非循環圖。然而由於目前資料庫的樣本數不足以用來建立高階的馬可夫鏈,所以如果要使用高階的馬可夫模型便會產生不適用的情形(overfitting)。因此為了避免不適用的情況發生,通常只能使用二階的馬可夫鏈,但也因此序列間位置的依賴關係便會形成循環性的圖,而用貝式網路來建立模型就會失去了一些位置間的依賴性。
在這篇論文中,便是先利用卡方統計(chi-square statistics)找出訊號位置間的依賴性而建立出關聯圖(dependency graph)做為剪接訊號的基本模型,接著利用貝式網路並允許每個位置可以表示成不同的點但限制每個點至多只能有兩個起源點來展開關聯圖。這個方法的好處是可以掌握了較多位置間的依賴性,但同時又不會發生不適用的問題,所以可以用來改善剪接訊號偵測以及基因識別的成果。

Owing to the progress of biochemical technologies, and the completion of the human genome project(HGP), a large amount of DNA or protein sequences have produced. In Bioinformatics, an important issue is to find the precise exon-intron boundaries of genes in human genomic DNA, usually called gene identification. There are many signals spreading in a gene. In this thesis we focus on the most important signals called splice sites.
A recent method used in the detection of splice signals is to model the signals by Bayesian networks. A Bayesian network can be described as a directed acyclic graph in which each node
represents a random variable. The edges express the direct
influences from parent nodes to child nodes. However, cyclic
dependency among positions cannot be described in such a
Bayesian network. This limits the capability of Bayesian network for the modeling of splice signals.
In this thesis, we first develop a dependency graph as the basic model of splice signals and then expand the graph by a Bayesian network by allowing the positions to appear more than once to capture their inter-dependencies but avoid overfitting.
The construction of the dependency graph is based on chi-square statistics to test the hypothesis of inter-dependency between positions. This method improves the performance of splice sites prediction and the gene identification system.

摘要…………………………………………………………………………………..i
誌謝………………………………………………………………………….………ii
目錄………………………………………………………………………….……….1
第一章 簡介………………………………………………………………………..2
第二章 前訊息核醣核酸剪接和基因識別………………………………………..3
第三章 建立在去氧核醣核酸基因組的剪接訊號模型…………………………..4
第四章 模型架構…………………………………………………………………..5
第五章 模擬結果…………………………………………………………………..6
第六章 結論與未來工作…………………………………………………………..7
附錄 英文論文範本………………………………………………………………....8

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