核醣核酸是普遍存在於有機體內的重要遺傳物質。和去氧核醣核酸不同的是，其結構為單股長鏈分子。在水溶液和生物體內，分布於鏈上的核甘酸因氫鍵作用力產生鍵結而形成分子內螺旋的二級結構。最簡單的核醣核酸二級結構稱之為巢狀結構。然而有些核醣核酸生成的二級結構較為複雜，我們稱之為偽結結構。現存許多核醣核酸的二級結構預測軟體的預測能力有限並各有所擅場。本研究的主要目標為整合現有之預測工具以提高整體核醣核酸二級結構預測的準確度。我們提出了一個核醣核酸序列分析法作為在選擇預測核醣核酸二級結構工具的預先處理。該分析法主要是藉由支援向量機的分類能力，達到預先選擇適宜該序列之預測工具的目的。本研究中所使用的核醣核酸序列資料由 PseudoBase 以及 RNA SSTRAND 兩個資料庫所得。利用交叉驗證的方法，我們一共測試了723筆真實存在的核醣核酸序列。實驗結果指出我們不僅提高了預測的整體準確率，並且使預測的敏感度和選擇性皆有所提升。

Ribonucleic acid (RNA) sometimes occurs in a complex structure called pseudoknots. Prediction of RNA secondary structures has drawn much attention from both biologists and computer scientists. Consequently, many useful tools have been developed for RNA secondary structure prediction, with or without pseudoknots. These tools have their individual strength and weakness. As a result, we propose a hybrid feature extraction method which integrates two prediction tools pknotsRG and NUPACK with a support vector machine (SVM). We first extract some useful features from the target RNA sequence, and then decide its prediction tool preference with SVM classification. Our test data set contains 723 RNA sequences, where 202 pseudoknotted RNA sequences are obtained from PseudoBase, and 521 nested RNA sequences are obtained from RNA SSTRAND. Experimental results show that our method improves not only the overall accuracy but also the sensitivity and the selectivity of the target sequences. Our method serves as a preprocessing process in analyzing RNA sequences before employing the RNA secondary structure prediction tools. The ability to combine the existing methods and make the prediction tools more accurate is our main contribution.

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1 Prediction of RNA Secondary Structure with Dynamic Programming . . . 6
2.1.1 pknotsRG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 NUPACK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 The Support Vector Machine (SVM) . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Kernel Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Soft Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.3 LIBSVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Chapter 3. Effective Features for RNA Structure Prediction . . . . . . . . . . 15
3.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.1.1 The Compositional Factor . . . . . . . . . . . . . . . . . . . . . 16
3.1.2 The Bi-transitional Factor . . . . . . . . . . . . . . . . . . . . . 17
3.1.3 The Distributional Factor . . . . . . . . . . . . . . . . . . . . . . 17
3.1.4 The Tri-transitional Factor . . . . . . . . . . . . . . . . . . . . . 18
3.1.5 The Potential Base-pairing Factor . . . . . . . . . . . . . . . . . 18
3.1.6 The Nucleotide Proportional Factor . . . . . . . . . . . . . . . . 18
3.1.7 The Potential Single-stranded Factor . . . . . . . . . . . . . . . . 19
3.1.8 The Sequence Specific Score . . . . . . . . . . . . . . . . . . . . 19
Page
3.1.9 The Segmental Factor . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.10 An Example of Feature Extraction . . . . . . . . . . . . . . . . . 21
3.2 Our Method with SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Chapter 4. Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1 The Source of Our Data and the Evaluation Criteria . . . . . . . . . . . . 28
4.2 The Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.2.1 Parameter Searching . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2.2 Self-Consistency Test . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.3 The Jackknife Test . . . . . . . . . . . . . . . . . . . . . . . . . 35
Chapter 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

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