將機器學習方法整合於生物資訊的研究有多年的歷史了。給定一條序列，此序列可以是由氨基酸或核苷酸所構成。如果序列是由氨基酸所構成，我們稱此序列為蛋白質序列。如果序列是由核苷酸所構成，我們稱此序列為核糖核酸序列。利用機器學習技術，可以在使用者不進行實驗的情況下，告訴他們這條序列的資訊，例如其折疊狀態為何或者是屬於那一類型蛋白質。在這篇論文中，我們主要將研究重點集中在核糖核酸二級結構預測以及必要蛋白質預測。

在核糖核酸二級結構預測問題中，我們主要目的是要去預測核糖核酸序列的折疊狀況。傳統的方式在進行預測時，通常是採用熱力學或序列比對的方法。在此論文，我們採用由其他學者已經開發的工具為基礎預測軟體，並利用已經訓練好的支持向量機當成選擇器，選擇到底應該用那一基礎軟體進行預測，整體的二級結構預測準確率較高。為了讓支持向量機可以選到較佳的基礎軟體，我們提出了漸進式的特徵選擇與分類器合法方法。實驗結果顯示，以這樣的方法所得到的二級結構預測正確率比我們採用的任一基礎軟體的正確率都有顯著的提升。

在必要蛋白質預測問題中，除了利用蛋白質序列資訊外，我們也採用了蛋白質交互作用網路以及其他蛋白質特性。為了能夠找到重要的特徵，我們提出了可以同時考慮到分類效能與特徵子集大小的循序向後特徵選擇方法。時驗結果顯示，採用我們的方法找到的特徵子集所訓練出來的支持向量機，其對蛋白質必要性的預測能力，較之前學者所得到的結果有顯著提升。

The machine learning approach has been adopted in bioinformatics for several decades. Given a sequence, which may be composed of nucleotides or amino acids, the problem is to ask the learning machine about the status of the sequence without performing experiments. In this dissertation, we focus on two problems of recent interest, which are the prediction of the RNA secondary structure, and the prediction of the protein essentiality.

An RNA secondary structure is the fold of a nucleotide sequence. Conventional methods usually address the structure prediction problem from the thermodynamics or comparative perspectives. Instead of developing our prediction tool from scratch, we take advantage of the state-of-the-art software tools. We adopt a tool preference choice approach to select a good software tool for prediction, in hope that the performance is better than any base prediction software. Our tool selector is built by incorporating various RNA sequence features and several SVM classifiers. To facilitate classifier combination and important feature identification, we propose an incremental feature selection method for classifier ensemble construction. The experimental results show that the achieved prediction accuracy is significantly better than any base predictor.

For the essential protein prediction problem, we also adopt various features, which include sequence, protein, topology, and other properties. To identify features relevant to the protein essentiality, we propose a modified sequential backward feature selection method. The method takes both feature sizes and prediction performance into consideration. The experimental results show that the achieved performance is significantly better than those of previous works.

1 Introduction 1
1.1 RNA Secondary Structure Prediction 1
1.2 Essential Protein Prediction 3
1.3 Summary and Organization 6
2 Prerequisite Knowledge 7
2.1 Position Specific Scoring Matrix (PSSM) 7
2.2 Support Vector Machine 8
2.3 Hierarchical Clustering 9
2.4 Cross-Validation Methods 10
2.4.1 k-fold Cross-Validation 11
2.4.2 Bootstrap Cross-Validation 11
2.5 Information-Theoretic Feature Selection Methods 12
2.5.1 Basic Information Theory and Feature Relevance 12
2.5.2 Minimal Redundancy and Maximal Relevance (mRMR) 15
2.5.3 Minimal Relevant Redundancy (mRR) 15
2.5.4 Conditional Mutual Information Maximization (CMIM) 16
2.6 Classifier Combination Methods 17
2.6.1 Majority Vote 17
2.6.2 Behavior Knowledge Space 18
2.6.3 Adaboost 19
2.7 Significance Tests 19
2.8 Performance Evaluation Methods 21
2.9 RNA Secondary Structure Prediction Softwares 21
2.9.1 pknotsRG 21
2.9.2 RNAStructure 22
2.9.3 NUPACK 22
3 Feature Extraction 23
3.1 Composition-Related Features 23
3.2 RNA Features 26
3.2.1 Transformed Protein Sequence Features 26
3.2.2 Sequence Features 28
3.2.3 Other Features 30
3.3 Protein Features 38
3.3.1 Sequence Features 38
3.3.2 Protein Property Features 42
3.3.3 Topology Features 43
3.3.4 Other Features 52
4 RNA Secondary Structure Prediction 54
4.1 Data sets and Features 55
4.2 An Incremental Feature Selection Method 57
4.3 Experimental Results 61
4.3.1 Experiments for the Classification Accuracy 61
4.3.2 Significance Tests for the Base-pair Accuracy and Feature Analysis 64
4.4 Discussion 68
5 Essential Protein Prediction 70
5.1 Data sets and Features 71
5.2 The Feature Selection Method 74
5.3 Experimental Results 76
5.3.1 The Experimental Procedure 76
5.3.2 Backward Feature Selection and mRMR/CMIM Feature Ranking 77
5.3.3 Bootstrap Cross-Validations 79
5.3.4 Performance Comparison and Significance Tests 84
5.3.5 ROC Analysis 88
5.3.6 Top Percentage Analysis 91
5.3.7 Confidence Intervals of Performance Measures and Informational Odds 96
5.3.8 Comparison with Other Feature Selection Methods 99
5.4 Discussion 105
6 Conclusions 109

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