臺灣博碩士論文加值系統

演化式演算法是一種強力的最佳化工具，並且已經廣泛的應用在生物資訊的領域中。對於包含有大量調整參數的預測問題，預測結果的精確度將取決於所使用的演化式演算法之搜尋能力。在本論文中，我們使用數種有效率的演化式計算方法解決以下的預測問題：模糊規則分類器的設計、蛋白質與小分子彈性嵌合，以及蛋白質結構類別預測。
首先，我們將模糊規則分類器設計上的構成元素轉換成為一個複雜的最佳化問題中的參數，並使用智慧型基因演算法有效的解決該模糊分類器設計時的參數調整問題。此設計方法具有三大優點：1)智慧型基因演算法的強大的搜尋能力可以找出具有高度適應值的模糊分類器，2) 產生有高度可讀性的模糊規則，3)產生的分類器對未知樣本有很高的辨識率。在11組數值資料的交互驗證實驗中，效能比較與統計分析的結果顯示以智慧型基因演算法為基礎的設計方法，能有效率的產生精簡與精確的模糊分類器，並且介紹該模糊分類器設計方法應用在基因表現資料的預測問題的實例。
蛋白質與小分子的彈性嵌合可被描述成一個最佳化問題，目標是預測小分子相對於蛋白質的平移、朝向、與構型參數，使得該嵌合結果具有最低的能量。對於含有大量可旋轉鍵結的高度彈性化合物，會因為其極大的構型搜尋空間與參數間的強烈關聯性，使得彈性嵌合的最佳化問題變得極為困難。本論文提出一個以粒子群最佳化為基礎的最佳化演算法SODOCK，用以解決蛋白質與小分子的彈性嵌合問題。模擬結果顯示，相較於幾種先進的嵌合方法，SODOCK能得到更精確的預測結果。
最後我們提出一套用於蛋白質結構類別預測的演化式特徵選擇方法。在原有胺基酸組成特徵中增加新的物化屬性，並配合適當的分類器，可以提高預測的精確度。然而要在大量物化屬性中，挑選出有助於預測的屬性子集合是相當困難的。本論文提出的演化式特徵選擇方法可以在胺基酸組成比例特徵與AAindex物化屬性資料庫中挑選出具高品質的特徵子集合。實驗結果顯示相較於胺基酸組成比例特徵，該子集合能有效提升貝氏分類器、支持向量機，與羅吉斯迴歸三種分類器的預測精確度。三種分類器採用演化式挑選的特徵子集合的平均預測精確度，亦優於使用現有以專家經驗挑選的66維特徵。

Evolutionary algorithm (EA) is a powerful optimization tool and has been widely in bioinformatics area. For a complex prediction problem which involved of large amount of tuning parameters, the prediction accuracy is dominated by the optimization performance of the used evolutionary algorithm. In this dissertation, we use several efficient evolutionary computation approaches to solve the following prediction problems: design of fuzzy rule-based classifier, flexible protein-ligand docking, and protein structural class prediction.
Firstly, an evolutionary approach to designing accurate classifiers with a compact fuzzy-rule base using a scatter partition of feature space is proposed, in which all the elements of the fuzzy classifier design problem have been moved in parameters of a complex optimization problem. An intelligent genetic algorithm (IGA) is used to effectively solve the design problem of fuzzy classifiers with many tuning parameters. The merits of the proposed method are threefold: 1) the proposed method has high search ability to efficiently find fuzzy rule-based systems with high fitness values, 2) obtained fuzzy rules have high interpretability, and 3) obtained compact classifiers have high classification accuracy on unseen test patterns. The performance comparison and statistical analysis of experimental results using ten-fold cross validation show that the IGA-based method without heuristics is efficient in designing accurate and compact fuzzy classifiers using 11 well-known data sets with numerical attribute values. Consequently, an application of the fuzzy classifier to a prediction problem in gene expression analysis is introduced.
Flexible protein-ligand docking can be formulated as a parameter optimization problem whose objective is to find the translation, orientation, and conformation of a ligand relative to the active site of a target protein with the lowest energy. For highly flexible ligands with a lot of rotatable bonds, the optimization problem of flexible docking would be more difficult due to the extremely large conformation space. We proposed a novel optimization algorithm, Swarm Optimization for flexible DOCKing (SODOCK), based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. The computer simulation results shown that SODOCK can obtain more accurate results, comparing with several state-of-the-art docking methods.
Finally, we propose an evolutionary feature selection approach based on inheritable intelligent genetic algorithm for the prediction of protein structural class. Adding physicochemical properties into protein features can improve the prediction accuracy of a proper classifier. However, selection of useful features from hundreds of physicochemical properties is very difficult. The proposed evolutionary feature selection method can obtain high quality feature subsets from amino acid composition and physicochemical properties AAindex. The experimental results show that the obtained feature subsets improve the prediction accuracies of naive Bayes classifier, support vector machine (SVM), and logistic regression, comparing with these classifiers using amino acid composition features alone. The average prediction accuracy of these classifiers with the obtained feature subsets are also superior to an existing 66-dimensional feature set designed by experts.

Acknowledgements i
中文摘要 ii
Abstract iii
Table of Contents v
List of Figures vii
List of Tables ix
Chapter 1 Introduction 1
1.1 Motivation 3
1.2 Achievements 4
1.3 Organization of this Dissertation 5
Chapter 2 Related Work 6
2.1 Fuzzy Rule-Based Classifiers 6
2.1.1 Fuzzy Partitions 6
2.1.2 Generation of Compact Fuzzy Rule Base 7
2.1.3 Approaches to Improving Classification Accuracy 8
2.2 Flexible Protein Ligand Docking 8
2.3 Protein Structural Class Prediction 9
Chapter 3 Overview of Optimization Algorithms 11
3.1 Genetic Algorithm 11
3.2 Orthogonal Experimental Design 12
3.3 Intelligent Genetic Algorithm 13
3.3.1 Intelligent Crossover 13
3.3.2 Procedure of IGA[18] 15
3.4 Particle Swarm Optimization 15
Chapter 4 Design of Fuzzy Rule-Based Classifier 19
4.1 Membership Function and Fuzzy Partition 19
4.2 Fuzzy Rule and Fuzzy Reasoning Method 20
4.3 Fitness Function and Chromosome Representation 22
4.4 Experimental Results 24
4.4.1 Sensitivity Analysis 24
4.4.2 Performance Comparisons 28
4.5 An Application to Microarray Data Analysis [81] 38
4.6 Summary 42
Chapter 5 Swarm Optimization for Flexible Protein-Ligand Docking 43
5.1 Problem Definition 43
5.1.1 Representation 43
5.1.2 Scoring Function 44
5.2 Hybrid Search of SODOCK 44
5.2.1 Local Search 44
5.2.2 Procedural of SODOCK 46
5.3 Analysis 47
5.3.1 Interactions among Encoded Parameters 47
5.3.2 Crossover of GA 48
5.3.3 Move Mechanism of PSO 49
5.4 Simulation Results 49
5.4.1 Data Preparation 49
5.4.2 Part 1�{Evaluation of Search Ability 50
5.4.3 Part 2�{Docking Accuracy 55
5.5 Summary 57
Chapter 6 Protein Structural Class Prediction 59
6.1 Protein Sequences Representation 59
6.1.1 Amino Acid Composition 59
6.1.2 AAindex 60
6.2 Evolutionary Feature Selection 61
6.2.1 Representation and Fitness Function 61
6.2.2 Inheritable Intelligent Genetic Algorithm 62
6.3 Experimental Results 65
6.4 Summary 70
Chapter 7 Conclusions and Future Work 71
7.1 Conclusions 71
7.2 Future Work 72
7.2.1 Flexible Protein-Ligand Docking. 72
7.2.2 Protein Structural Class Prediction 72
Reference 74

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