博碩士論文 975202073 詳細資訊




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姓名 朱致遠(Chih-Yuan Chu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 利用支持向量機與機率矩陣以序列預測蛋白質與蛋白質交互作用之接合點
(Sequence-based prediction of protein-protein interaction sites using probability matrix and support vector machine)
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摘要(中) 蛋白質與蛋白質交互作用在生物功能上扮演了很重要的角色,而分析蛋白質與蛋白質交互作用時的接合點是一種研究蛋白質與蛋白質交互作用的普遍方法。此外對蛋白質的接合研究以及藥物設計等等都有很大的幫助。
胺基酸殘基的組成對一個殘基是否成為一個接很點有很大的影響力,我們嘗試蒐集了大量的胺基酸組合資料以作為預測之用。我們提出這個方法的基本概念就是當殘基在特定組合中過去偏向於有接合點發生時,在未來於相同組合中應也偏向於發生接合,反之亦然。
我們和其他三篇研究作出比較,並且都顯示出我們的方法有較高的準確率。而平均靈敏度以及特異度都接近九成。
雖然我們的結果較好,但我們的方法可以說是暴力解,在利用支持向量機前,我們先預先收集並統計了萬筆的蛋白質序列資料。
若忽視我們的方法可能需要大量的電腦記憶體,我們提供了一個高準確率的蛋白質與蛋白質交互作用接合點的預測方法。
摘要(英) Protein-protein interaction plays a key role in many biological functions. Identifying protein-protein interaction site is a common way to understand protein-protein interaction and is helpful in docking studies, drug design and so on. Since amino acid residue combination has a great related to interaction site forming, we tried to collect and calculate large amount of combination for predicting usage. The basic idea in our method is residues in certain combinations will prefer interaction if it acted as interaction sites more often before in same combinations, vice versa. We compared our method with other three studies and performed better in all cases. The average of sensitivity and specificity were both nearly 90%. Although our performance was better than others, our method was nearly a brute force since we collect and calculate nearly ten thousand protein sequences before. If ignore the fact that our method may needed large computer memory, we provide a highly accuracy method for protein-protein interaction site prediction.
關鍵字(中) ★ 蛋白質與蛋白質交互作用接合點 關鍵字(英) ★ protein-protein interaction sites
論文目次 CHAPTER 1 INTRODUCTION ............................................................................. 1
1.1 BACKGROUND ....................................................................................................... 1
1.2 MOTIVATION.......................................................................................................... 2
1.3 GOAL .................................................................................................................... 2
CHAPTER 2 RELATED WORKS .......................................................................... 3
2.1 PREDICTION OF PROTEIN-PROTEIN INTERACTION SITES BY SVM ........................... 3
CHAPTER 3 MATERIALS AND METHODS ...................................................... 6
3.1 DATA SOURCES ...................................................................................................... 6
3.2 SYSTEM FLOW ....................................................................................................... 6
3.3 METHODS .............................................................................................................. 6
CHAPTER 4 RESULTS ......................................................................................... 17
4.1 EVALUATION FORMULA ....................................................................................... 17
4.2 COMPARE WITH OTHER STUDIES. ......................................................................... 17
CHAPTER 5 DISCUSSION................................................................................... 20
5.1 CASE STUDIES ..................................................................................................... 20
5.2 WHY OUR METHOD WORKS? ................................................................................ 22
5.3 WITHOUT SVM? ................................................................................................. 25
5.4 FUTURE WORKS ................................................................................................... 27
REFERENCES ........................................................................................................... 28
APPENDIX A ............................................................................................................. 30
參考文獻 1. Porollo, A. and J. Meller, Prediction-based fingerprints of protein-protein interactions. Proteins-Structure Function and Bioinformatics, 2007. 66(3): p. 630-645.
2. Liu, B., et al., Prediction of protein binding sites in protein structures using hidden Markov support vector machine. Bmc Bioinformatics, 2009. 10: p. 381-394.
3. Gallet, X., et al., A fast method to predict protein interaction sites from sequences. Journal of Molecular Biology, 2000. 302(4): p. 917-926.
4. Zhou, H.X. and Y.B. Shan, Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins-Structure Function and Genetics, 2001. 44(3): p. 336-343.
5. Konc, J. and D. Janezic, Protein-protein binding-sites prediction by protein surface structure conservation. Journal of Chemical Information and Modeling, 2007. 47(3): p. 940-944.
6. Du, X.Q., J.X. Cheng, and J. Song, Identifying Protein-Protein Interaction Sites Using Covering Algorithm. International Journal of Molecular Sciences, 2009. 10(5): p. 2190-2202.
7. Koike, A. and T. Takagi, Prediction of protein-protein interaction sites using support vector machines. Protein Engineering Design & Selection, 2004. 17(2): p. 165-173.
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9. Wang, B., et al., Predicting protein interaction sites from residue spatial sequence profile and evolution rate. Febs Letters, 2006. 580(2): p. 380-4.
10. Sikic, M., S. Tomic, and K. Vlahovicek, Prediction of protein-protein interaction sites in sequences and 3D structures by random forests. Plos Computational Biology, 2009. 5(1): p. e1000278.
11. Chen, X.W. and J.C. Jeong, Sequence-based prediction of protein interaction sites with an integrative method. Bioinformatics, 2009. 25(5): p. 585-91.
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13. Ofran, Y. and B. Rost, Predicted protein-protein interaction sites from local sequence information. Febs Letters, 2003. 544(1-3): p. 236-239.
14. Ofran, Y. and B. Rost, ISIS: interaction sites identified from sequence. Bioinformatics, 2007. 23(2): p. E13-E16.
15. Lin, C.-C.C.a.C.-J., LIBSVM: a library for support vector machines. 2001.
16. Akbani, R., S. Kwek, and N. Japkowicz, Applying support vector machines to imbalanced datasets. Machine Learning: Ecml 2004, Proceedings, 2004. 3201: p. 39-50.
指導教授 吳立青、洪炯宗
(Li-Ching Wu、Jorng-Tzong Horng)
審核日期 2010-7-27
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