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論文名稱 Title |
利用混合模型之雙硫鍵預測方法 Disulfide Bond Prediction with Hybrid Models |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
42 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2011-08-31 |
繳交日期 Date of Submission |
2011-09-06 |
關鍵字 Keywords |
半胱胺酸、雙硫鍵、混合模型、支持向量機、預測 prediction, SVM, disulfide bond, cysteine, hybrid model |
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統計 Statistics |
本論文已被瀏覽 5636 次,被下載 1132 次 The thesis/dissertation has been browsed 5636 times, has been downloaded 1132 times. |
中文摘要 |
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。 |
Abstract |
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. |
目次 Table of Contents |
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 |
參考文獻 References |
[1] S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipmanl, “Basic local alignment search tool,” Journal of Molecular Biology, Vol. 215, No. 3, pp. 403–410, 1990. [2] S. F. Altschul, T. L. Madden, A. A. Schaffer, J. Zhang, Z. Zhang, W. Miller, and D. J. Lipman, “Gapped blast and psi-blast: a new generation of protein database search programs,” Nucleic Acids Research, Vol. 25, No. 17, pp. 3389– 3402, 1997. [3] C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. [4] H. Y. Chang, C. B. Yang, and H. Y. Ann, “Refinement on o atom positions for protein backbone prediction,” Proceedings of the 2nd WSEAS International Conference on BIOMEDICAL ELECTRONICS and BIOMEDICAL INFOR- MATICS, 2009. [5] Y.-C. Chen and J.-K. Hwang, “Prediction of disulfide connectivity from protein sequences,” PROTEINS: Structure, Function, and Genetics, Vol. 61, pp. 507– 512, 2005. [6] Y.-C. Chen, Y.-S. Lin, C.-J. Lin, and J.-K. Hwang, “Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences,” PROTEINS: Structure, Function, and Genetics, Vol. 55, pp. 1036–1042, 2004. [7] W.-C. Chung, “A multi-phase approach for disulfide bond prediction,” Master’s Thesis, Department of Computer Science and Engineering, National Sun Yat- Sen University, Kaohsiung, Taiwan, 2009. [8] W.-C. Chung, C.-B. Yang, and C.-Y. Hor, “An effective tuning method for cys- teine state classification,” Proc. of National Computer Symposium, Workshop on Algorithms and Bioinformatics, Taipei, Taiwan, Nov. 27-28, 2009. [9] P. Frasconi, A. Passerini, and A. Vullo, “A two-stage svm architecture for predicting the disulfide bonding state of cysteines,” Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on, pp. 25–34, 2002. [10] D. T. Jones, “Protein secondary structure prediction based on position-specific scoring matrices,” Journal of Molecular Biology, Vol. 292, No. 2, pp. 195–202, 1999. [11] W. Kabsch and C. Sander, “Dictionary of protein secondary structure: Pat- tern recognition of hydrogen-bonded and geometrical features,” Biopolymers, Vol. 22, pp. 2577–2637, 1983. [12] J. R. G. L., A. P. Shilton, M. M. Parker, and M. Palaniswami, “Prediction of cystine connectivity using SVM,” Bioinformation, Vol. 1, No. 2, pp. 69–74, 2005. [13] H.-L. Liu and S.-C. Chen, “Prediction of disulfide connectivity in proteins with support vector machine,” Journal of the Chinese Institute of Chemical Engi- neers, Vol. 38, No. 1, pp. 63–70, 2007. [14] C.-H. Lu, Y.-C. Chen, C.-S. Yu, and J.-K. Hwang, “Predicting disulfide con- nectivity patterns,” PROTEINS: Structure, Function, and Genetics, Vol. 67, pp. 262–270, 2007. [15] R. Singh, “A review of algorithmic techniques for disulfide-bond determina- tion,” Brief Funct Genomic Proteomic, Vol. 7, No. 2, pp. 157–172, 2008. [16] J. Song, Z. Yuan, H. Tan, T. Huber, and K. Burrage, “Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure,” Bioinformatics, Vol. 23, No. 23, pp. 3147–3154, 2007. [17] C.-H. Tsai, B.-J. Chen, C.-H. Chan, H.-L. Liu, and C.-Y. Kao, “Improving disulfide connectivity prediction with sequential distance between oxidized cys- teines,” Bioinformatics, Vol. 21, No. 24, pp. 4416–4419, 2005. [18] M. Vincent, A. Passerini, M. Labbe, and P. Frasconi, “A simplified approach to disulfide connectivity prediction from protein sequences,” BMC Bioinformatics, Vol. 9, No. 1, p. 20, 2008. [19] L. Zhu, J. Yang, J.-N. Song, K.-C. Chou, and H.-B. Shen, “Cysteine separa- tions profiles (csp) on protein sequences infer disulfide connectivity,” Journal of Computational Chemistry, Vol. 31, No. 7, pp. 1415–1420, 2009. |
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