雙硫鍵是存在於蛋白質裡兩個半胱胺酸之間的一種特殊共價鍵。這種共價鍵
對蛋白質的折疊跟穩定作用上扮演很重要的角色。在雙硫鍵連結模式預測這一方
面，可能的連接模式會因半胱胺酸數量的增加而急速成長，而成為一個難題。在
這篇論文中，我們針對這個問題提出了一個改進方法。這個方法是以支持向量機
作為基礎的模型方法。經由這個策略，我們可以藉由選取適合的模型來增加預測
的準確度。為了評估我們方法的效能，我們使用SP39 這個擁有446 條蛋白質的
資料集，並採用4 次交叉驗證的方法。我們在配對模式跟連結模式上個別達到
70.8%以及65.9%，且得到比前人較好的成果。

Disulfide bonds are special covalent cross links between two cysteines in a
protein. This kind of bonding state plays an important role in protein folding and
stabilization. For connectivity pattern prediction, it is a very difficult problem because
of the fast growth of possible patterns with respect to the number of cysteines. In this
thesis, we propose a new approach to address this problem. The method is based on
hybrid models with SVM. Via this strategy, we can improve the prediction accuracies
by selecting appropriate models. In order to evaluate the performance of our method,
we apply the method by 4-fold cross-validation on SP39 dataset, which contains 446
proteins. We achieve accuracies with 70.8% and 65.9% for pair-wise and pattern-wise
prediction respectively, which is better than the previous works.

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1 Position-Specific Score Matrix . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Secondary Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.4 Previous Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4.1 Chung’s Method with Down-sampling . . . . . . . . . . . . . . 10
2.4.2 Song’s Method with Secondary Structure Information . . . . . 11
2.4.3 Zhu’s Method Using Feature Selection . . . . . . . . . . . . . 12
Chapter 3. Algorithms for Disulfide Bond Prediction . . . . . . . . . 13
3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Algorithm for Connectivity Prediction . . . . . . . . . . . . . . . . . 15
Chapter 4. Experimental Results . . . . . . . . . . . . . . . . . . . . . . 19
4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3 Connectivity Prediction Experiments . . . . . . . . . . . . . . . . . . 20
Chapter 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

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