在這篇論文，在事先給予半胱胺酸的鍵結情形下我們提出了一個新方法來解決蛋白質雙硫鍵的鍵結圖形問題。
我們的方法首先利用整體的半胱胺酸資訊來預測半胱胺酸的標籤，然後使用該標籤預測的結果來預測整體的雙硫鍵鍵結圖形。
如實驗結果顯示的，在相同的訓練資料之下，我們的方法相較於其他的方法擁有較高的準確度。
更進一步的，為了提升我們方法的準確度，我們使用半胱胺酸隔離輪廓方法與行為知識空間來建立我們的混合方法來；該混合方法是由陳等學者所提出。

There are four versions for the disulfide prediction problem, including
chain classification, bonding state of each cysteine, connectivity of each cysteines pair and disulfide bonding pattern.
Among these problems, the prediction of disulfide bonding pattern is the most difficult, especially for the proteins with four or more disulfide bonds.
In that case, a large amount of training proteins should be collected in order to obtain a reliable prediction model.
In this thesis, we propose a novel algorithm to solve the disulfide bonding pattern problem for the proteins that the bonding states of cysteines are given.
Our method first predicts the labels of cysteines with global information, and then uses the label prediction results to predict the whole disulfide bonding pattern.
As the experimental results show, our method achieves a higher accuracy than other previous methods for given the same training dataset.
Furthermore, to improve the accuracy of our method, we use the CSP method and the BKS table to build our hybrid model, which was proposed by Chen et al.

中文審定書 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
英文審定書 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
謝辭 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
TABLE OF CONTENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Proteins and Amino Acid Residues . . . . . . . . . . . . . . . . . . . 5
2.2 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Position-Specific Scoring Matrix . . . . . . . . . . . . . . . . . . . . . 7
2.4 The Behavior Knowledge Space Method . . . . . . . . . . . . . . . . 8
2.5 Previous Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.5.1 Cysteine Separations Profiles . . . . . . . . . . . . . . . . . . 9
2.5.2 Chen's Behavior Knowledge Space with SVM Methods . . . . 10
2.5.3 Lin's Method with Structural Information and Multiple Tra-
jectory Search . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Chapter 3. Our Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1 Cysteine Label Prediction . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 The Pairwise Method with Label Prediction Results . . . . . . . . . . 15
3.3 The Hybrid Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Chapter 4. Experimental Results . . . . . . . . . . . . . . . . . . . . . . 21
4.1 Datasets and Performance Evaluation . . . . . . . . . . . . . . . . . . 21
4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Chapter 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

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