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研究生:梁景評
研究生(外文):Ching-Ping Liang
論文名稱:基於特徵權重及模糊決策樹的插入子辨識方法
論文名稱(外文):Intron Identification Approaches Based on Weighted Features and Fuzzy Decision Trees
指導教授:黃胤傅
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊工程研究所
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:2009
畢業學年度:97
語文別:英文
論文頁數:34
中文關鍵詞:插入子辨識模糊決策樹自我組織映射插入子序列特徵計算導向插入子定義模型
外文關鍵詞:intronic sequence featurecomputation-oriented intron definition modelIntron identificationself-organizing mapfuzzy decision tree
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現今計算基因接合點(Splice sites)的預測方法大部分依賴傳統已知的插入子定義模型(IDM)的特徵。以計算為導向的插入字定義模型(CO-IDM)清楚地提供更明確和具體的資訊來描述接合點插入子兩側的基因序列(IFSSs)。在本論文中,我們提供了使用12個單一框架(UFPs)和45個多框架樣式(MFPs)的特徵和增益比率來挑選特徵的新模糊決策樹方法來改善插入子的辨識。首先,我們先將基因序列取出特徵並使用非監督式的自我組織式映射(SOM)的方法來做模糊化。接下來使用不同觀點的全域加權和交叉參考可讓生物學家解讀及辨識是否為插入子的規則產生方式。最後實驗結果證明所提出的方法有助於改善辨識的準確度。此外我們也實作一個線上的分類器來辨識未知的基因序列是否為插入子(Intron)。
Current computational predictions of splice sites largely depend on the sequence patterns of known intronic sequence features (ISFs) described in the classical intron definition model (IDM). The computation-oriented IDM (CO-IDM) clearly provides more specific and concrete information for describing intron flanks of splice sites (IFSSs). In the thesis, we proposed a novel approach of fuzzy decision trees (FDTs) which utilize 1) weighted ISFs of twelve uni-frame patterns (UFPs) and forty-five multi-frame patterns (MFPs) and 2) gain ratios to improve the performances in identifying an intron. First, we fuzzified extracted features from genomic sequences using membership functions with an unsupervised self-organizing map (SOM) technique. Then, we brought in different viewpoints of globally weighting and crossly referring in generating fuzzy rules which are interpretable and useful for biologists to verify whether a sequence is an intron or not. Finally, the experimental results revealed the effectiveness of the proposed method in improving the identification accuracy. Besides, we also implemented an on-line intronic identifier to infer an unknown genomic sequence.
中文摘要 i
英文摘要 ii
誌謝 iii
Contents iv
List of Tables v
List of Figures vi
1 Introduction 1
2 Related Work 3
2.1 Introns 3
2.2 Sequence Features 4
2.3 Fuzzy Theory 6
2.3.1 Fuzzy Sets 6
2.3.2 Fuzzy Logic 7
2.3.3 Fuzzy System 7
3 Fuzzy Decision Trees (FDTs) 10
3.1 Overview of the Proposed Approach 10
3.2 FDT Construction 10
3.2.1 Kohonen Feature Maps (SOM) 10
3.2.2 Weighted Fuzzy C4.5 13
3.3 Rule Generation 16
3.4 Fuzzy Inference 17
4 Performance Evaluations 19
4.1 Datasets and Measures 19
4.2 Experiments 19
5 Conclusions 24
Reference 25
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