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.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0906111-171625 |
| Date | 06 September 2011 |
| Creators | Wang, Chong-Jie |
| Contributors | Kuo-Tsung Tseng, Chang-Biau Yang, Jen-Sen Lin, Chia-Ning Yang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906111-171625 |
| Rights | user_define, Copyright information available at source archive |
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