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Title: 無限脈衝響應濾波器庫應用於基因啟動子序列之識別
Identification of Promoter Sequences Using IIR Lowpass Filter Banks
Authors: Ming-Zong Ye
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: 生物資訊;啟動子預測;庫貝克-李柏距離;馬可夫鏈;類神經網路;實數型基因演算法
Bioinformatics;Promoter prediction;Kullback Leibler's Distance;Markov chain;Neural networks;Real-Valued Genetic Algorithm
Date: 2006
Issue Date: 2011-07-04
Abstract: 摘要 自從1980年以來,分子生物學快速發展,加上接踵而來的「人類基因組計畫」,快速地累積了大量DNA序列及蛋白結構方面的數據;同時越來越多相關的資料庫也陸續被建構。如何有效的定義與維持這些資料是個極為重要的課題。雖然我們可以藉由生物實驗得知DNA序列是否被轉錄,但通常必須花費相當多的時間及成本。如何設計演算法則和開發相關程式軟體以分析和定義基因序列為現今迫切需要的研究之ㄧ。啟動子為DNA轉錄成RNA及調控基因表現的重要關鍵。經由啟動子的研究,我們不僅可以預測DNA序列可否被轉錄成RNA,甚至能更進一步預測DNA序列可轉錄成何種特定RNA。本論文旨在發展出一套快速有效的啟動子預測法則。首先我們提出一馬可夫-類神經網路以識別一段未知DNA序列是否為啟動子。同時當我們有一段完整的基因序列時,我們希望能更進一步尋找出啟動子可能的所在位置。在本論文中,我們將利用庫貝克-李柏距離及無限脈衝響應濾波器庫來預測啟動子所在位置。接著為了解決庫貝克-李柏距離在參數選擇上的問題,我們將引入了實數型的基因演算法以搜尋適當的視窗大小,進而建立以實數型基因演算法為基礎的貝克-李柏距離法則。本論文使用大腸桿菌定序資料作為結果驗證,且將藉由分類及啟動子序列定位來進行分析與預測。經由模擬結果顯示,所提方法除具啟動子分類精確辨識率外,並可有效預測出基因序列中啟動子之相關位置。 關鍵字: 生物資訊、啟動子預測、庫貝克-李柏距離、馬可夫鏈、類神經網路、實數型基因演算法
Abstract Molecular biology has been developed quickly since 1980 and follows the Human Genome Project (HGP, 2001) which has accumulated a large number of data about DNA sequences and protein structures. More and more relative valued databases are developed and it is important to maintain and annotate such data. Whether a DNA sequence transcribed or not can be verified by biological experiments, but those experiments often need much time and cost. How to design good computer algorithms and software to analyze and annotate gene sequences becomes one of the most important subjects today. Promoters are the transcription signals, which regulate the gene expressions, and are responsible for the transcription from DNA to RNA. Through the study on promoters, we can find out which DNA sequence will be transcribed into RNA, and we can even transcribe any DNA sequence which we intend to study into RNA. The goal of this thesis is to develop efficient prediction algorithms such that the promoter sequences can be identified accurately. Firstly, we propose the Markov-Neural networks to identify whether an unknown segment is promoter set or not. Moreover when we have a complete gene sequence, we want to find the possible locations of the promoter regions. The Kullback Leibler’s Distance (KLD) and IIR lowpass filter banks will be used to find the promoter locations of the considered sequence. To proceed, in order to solve the problem of parameter selection in KLD, we introduce the Real-Valued Gene Algorithm (RGA) to find an appropriate window size, and then the RGA-based KLD method can be established. In this thesis, the Escherichia Coli (E. coli) will be taken as our datasets and the performances of the proposed algorithms will be verified by classifying and locating the promoters of the E. coli sequences. It follows from the simulation results that our proposed methods can not only improve the accuracy in distinguishing between promoter sets and non-promoter sets but also predict probable promoter locations effectively. Keywords: Bioinformatics, Promoter prediction, Kullback Leibler’s Distance, Markov chain, Neural networks, Real-Valued Genetic Algorithm
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0M94670003
Appears in Collections:[通訊與導航工程學系] 博碩士論文

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