在預測蛋白質的三維結構及功能上，雙硫鍵 (Disulfide
bond) 扮演著關鍵的角色。本篇論文中提出了二個預測蛋白質中所有半胱氨酸 (Cysteine) 之氧化狀態的演算法。這些方法是基於使用多個支持向量機 (support vector machine)的多階段架構的演算法。第一各演算法在資料集PDB4136 上預測半胱氨酸之氧化狀態得到了94% 的準確度，但是在其他資料集的預測結果並不如預期。因此，設計第二各演算法提高同時擁有氧化及還原狀態之半胱氨酸的蛋白質的預測準確度。此外，更提出一個新的訓練策略來提升預測精度。這個訓練策略將支持向量機所找出的機率增加到目前現有的特徵中，然後開始一輪新的訓練，以此來得到更好的預測效能。所有實驗的資料集都是由眾所皆知資料庫所產生的，例如Protein Data Bank 及SWISS-PROT 等資料庫。結果在資料集PDB4136 上預測半胱氨酸之氧化狀態得到了94.3% 的準確度，比先前最好的結果90.7% 提高了3.6% 的預測精度。

Disulfide bonds play crucial roles to predict the three-dimensional structure and the function of a protein. This thesis develops two algorithms to predict the disulfide bonding state of each cysteine in a protein sequence. These methods are based on the multi-stage framework and the multi-classifier of the support vector machine (SVM). The first algorithm achieves 94.0% accuracy of cysteine state prediction for dataset PDB4136, but in some datasets the results are not as good as our expectation. Thus the second algorithm is designed to improve the predicting ability for the proteins which have oxidized and reduced cysteines simultaneously. In addition,
a new training strategy is also developed to increase the prediction accuracy. It appends the probabilities which are obtained from the SVM to the existing features and then starts a new training procedure repeatedly to get better performance. The experiments are performed on the datasets derived from well-known databases, such as Protein Data Bank and SWISS-PROT. It gets 94.3% accuracy for predicting disulfide bonding state on dataset PDB4136, which gets improvement 3.6% compared with the previously best result 90.7%.

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 Position Specific Scoring Matrix . . . . . . . . . . . . . . . . . . . . . 4
2.2 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Previous Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 Hidden Neural Networks . . . . . . . . . . . . . . . . . . . . . 10
2.3.2 Cysteine State Sequence . . . . . . . . . . . . . . . . . . . . . 12
2.3.3 APTK and DISULFIND . . . . . . . . . . . . . . . . . . . . . 13
2.4 The Two-stage System . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5 State Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Chapter 3. Our Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.1 Algorithm One . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 Algorithm Two . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 Probability Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4.1 Type Classification . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4.2 Mix and State Classification . . . . . . . . . . . . . . . . . . . 27
3.4.3 Normalization of Features . . . . . . . . . . . . . . . . . . . . 28
Chapter 4. Experimental Results . . . . . . . . . . . . . . . . . . . . . . 30
4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Chapter 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

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