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研究生:高國慶
研究生(外文):Kao, Kuo-Ching
論文名稱:利用蛋白質交互作用網路預測蛋白質功能之方法研究
論文名稱(外文):Protein function prediction using protein interaction networks
指導教授:黃俊燕
指導教授(外文):Huang, Jiun-Yan
學位類別:碩士
校院名稱:中華大學
系所名稱:生物資訊學系(所)
學門:生命科學學門
學類:微生物學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:38
中文關鍵詞:功能關聯函數功能機率函數蛋白質交互作用網路
外文關鍵詞:Functional correlationFunctional probabilityProtein interaction network
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  • 被引用被引用:0
  • 點閱點閱:236
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  • 下載下載:5
  • 收藏至我的研究室書目清單書目收藏:0
我們使用功能關聯函數衡量蛋白質交互作用網路中,擁有交互作用關係的蛋白質之間其生物功能緊密程度,並將蛋白質交互作用網路裡生物功能未被生物實驗研究出來的蛋白質給與隨機的功能機率分佈,再以功能關聯函數調整這些功能機率分佈使得功能關聯函數最大。最後,我們利用此機率分佈設定門檻值,並以某一未知生物功能蛋白質其功能機率是否超過此值來判定未知生物功能蛋白質是否具有此生物功能。
我們以目前蛋白質研究較為完備之物種-酵母菌為測試對象,將酵母菌蛋白質交互作用網路資料做干擾測試,我們發現高功能關聯函數之蛋白質的預測結果較低功能關聯函數之蛋白質好,並且我們使用不同來源之蛋白質交互作用網路資料的結果都顯示我們方法的預測結果與錯誤容忍力較他種方法好。
In this thesis, we used a quantity called functional correlation to evaluate functional closeness of a protein with its interacting partners. Yeast was used as a model organism, the protein-protein interaction data was downloaded from DIP. Each unknown function proteins was assigned a function probability distribution, we iteratively adjusted function probability distribution until average functional correlation among these unknown proteins reached maximum. A function was assigned to an unknown protein if its function probability is higher than the chosen threshold.
Our results showed that the correctness of predicted functions are higher for large degree and high correlated proteins. Our method is more robust to errors of protein interaction and fraction of known function proteins.
中文摘要 1
Abstract 2
目錄 3
圖目錄 4
表目錄 5
第一章 序論 6
1-1 研究背景 6
1-2 相關研究 6
1-2-1 直觀法(Direct methods) 7
1-2-2 模組輔助法(Module-assisted methods) 7
1-3 研究目的 8
第二章 資料與模型 9
2-1 資料來源 9
2-1-1 蛋白質交互作用網路資料 9
2-1-2 功能註解 10
2-2 蛋白質功能預測方法 12
2-2-1 全域最佳化方法(Global optimization method) 12
2-2-2 功能關聯函數(Functional correlation) 13
2-3 功能關聯函數最佳化方法(Functional correlation optimization method) 15
第三章 結果與討論 18
3-1 基準測試 18
3-3 隨機測試(Randomization test) 33
第四章 結論 36
參考文獻 38
[1] Sharan, R.,Ulitsky, I., and Shamir, R. (2007) Network-based prediction of protein function, Mol. Syst. Biol., 3:88.
[2] Schwikowski, B., Uet, P., and Fields, S. (2000) A network of protein–protein interactions in yeast, Nat. Biotech., 18, 1257-1261.
[3] Hishihaki, H. et al. (2001) Assessment of prediction accuracy of protein function from protein-protein interaction data, Yeast, 18, 523-531.
[4] Vazquez, A., Flammini, A., Maritan, A., and Vespignani, A. (2003) Global protein function prediction from protein–protein interaction networks, Nat. biotech., 21, 697-100.
[5] Leone, M., and Pegnani, A. (2005) Predicting protein function though message passing algorithms, Bioinformatics, 21, 239-247.
[6] Sun, S., Zhao, Y., Jiao, Y., Yin, Y., Cai, L., Zhang, Y., Lu, H., Chen, R., and Bu, D. (2006) Faster and more accurate protein function assignment from protein interaction networks using the MFGO algorithm, FEBS Lett., 580, 1891-1896.
[7] Karaoz, U., Murali, T.M., Letovsky, S., Zheng, Y., Ding, C., Cantor, C., and Kasif, S. (2004) Whole-genome annotation by using evidence integration in functional-linkage networks, Proc. Nat. Acad. Sci. USA, 101, 2888-2893.
[8] Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., and Singh, M. (2005) Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps, Bioinformatics, 21, i1-i9.
[9] Letovsky, S., and Kasif, S. (2003) Predicting protein function from protein/protein interaction data: a probabilistic approach, Bioinformatics, 19, i197-i204.

[10] Deng, M., Zhang, K., Mehta, S., Chen, T., and Sun, F. (2003) Prediction of protein function using protein-protein interaction data, J. Compt. Biol., 10, 974-960.
[11] Chua, H.N. Sung, W.K. and Wong, I., (2006) Exploiting indirect neighbors and topological weight to predict protein function from protein–protein interactions, Bioinformatics, 22, 1623-1630.
[12] Hartwell, L., Hopfield, J., Leibler, S., and Murray, A. (1999) From molecular to modular cell biology, Nature. 402. C47-C52.
[13] Yook, S., Oltvai, Z.N., and Barabasi, A.L., (2004) Functional and topological characterization of protein interaction networks, Proteomics, 4, 928-942.
[14] Arnau, V., Marsm S., Marin, I. (2005) Iterative cluster analysis of protein interaction data, Bioinformatics, 21, 364-378.
[15] Hartigan, J.A. (1975) Clustering Algorithms, New York, Wiley.
[16] Huang, J.Y. (2009) Tomography of functional organization in protein-protein interaction network, Physica A, 388, 2072-2080.
[17] Salwinski, L., Miller, C.S., Smith, A.J., Pettit, F.K., Bowie, J.U., Eisenberg, D. (2004) The Database of Interacting Proteins: 2004 update, NAR, 32 Database issue, D449-51.
[18] von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S., and Bork, P. (2002) Comparative assessment of large-scale data sets of protein–protein interactions, Nature, 417, 399–403.
[19] Güldener, U., Münsterkötter, M., Kastenmüller, G., Strack, N., van Helden, J., Lemer, C., Richelles, J., Wodak, S.J., Garcia-Martinez, J., Perez-Ortin, J.E., Michael, H., Kaps, A., Talla, E., Dujon, B., Andre, B., Souciet, J.L., De Montigny, J., Bon E, Gaillardin, C., Mewes, H.W. (2005) CYGD: the Comprehensive Yeast Genome Database. Nucleic Acids Research, 33, Database issue:D364-8.
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