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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 資訊管理學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29703
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor李瑞庭
dc.contributor.authorChia-Ming Hsuen
dc.contributor.author許家銘zh_TW
dc.date.accessioned2021-06-13T01:15:29Z-
dc.date.available2008-07-23
dc.date.copyright2007-07-23
dc.date.issued2007
dc.date.submitted2007-07-19
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[12] Gasch, A.P. and Eisen, M.B., “Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering,” Genome Biology, Vol. 3, No. 11, R59, 2002.
[13] Gavin, A.C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J.M., Michon, A.M., Cruciat, C.M., Remor, M., Hofert, C., Schelder, M., Brajenovic, M., Ruffner, H., Merino, A., Klein, K., Hudak, M., Dickson, D., Rudi, T., Gnau1, V., Bauch, A., Bastuck, S., Huhse, B., Leutwein, C., Heurtier, M.A., Copley, R.R., Edelmann, A., Querfurth, E., Rybin, V., Drewes, G., Raida, M., Bouwmeester, T., Bork, P., Seraphin, B., Kuster, B., Neubauer, G. and Superti-Furga, G., “Functional organization of the yeast proteome by systemic analysis of protein complexes,” Nature, Vol. 415, pp. 141-147, 2002.
[14] Girvan, M. and Newman, M.E., “Community structure in social and biological networks,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 99, pp. 7821-7826, 2002.
[15] Goldberg, D.S. and Roth, F.R., “Assessing experimentally derived interaction in a small world,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 100, pp. 4372-4376, 2003
[16] Hartuv, E. and Shamir, R., “A clustering algorithm based on graph connectivity,” Information Processing Letters, Vol. 76, pp. 175-181, 2000.
[17] Hartwell, L.H., Hopfield, J.J., Leibler, S. and Murray, A.W., “From molecular to modular cell biology,” Nature, Vol. 402, pp. c47-c52, 1999.
[18] Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., Adams, S., Millar, A., Taylor, P., Bennett, K., Boutilier, K., Yang, L., Wolting, C., Donaldson, I., Schandorff, S., Shewnarane, J., Vo, M., Taggart, J., Goudreault, M., Muskat, B., Alfarano, C., Dewar, D., Lin, Z., Michalickova, K., Willems, A.R., Sassi, H., Nielsen, P.A., Rasmussen, K.J., Andersen, J.R., Johansen, L.E., Hansen, L.H., Jespersen, H., Podtelejnikov, A., Nielsen, E., Crawford, J., Poulsen, V., Sorensen, B.D., Matthiesen, J., Hendrickson, R.C., Gleeson, F., Pawson, T., Moran, M.F., Durocher, D., Mann, M., Hogue, C.W.V., Figeys, D. and Tyers, M., “Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry,” Nature, Vol. 415, pp. 180-183, 2002.
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[20] Hong E.L., Balakrishnan R., Christie K.R., Costanzo M.C., Dwight S.S., Engel S.R., Fisk D.G., Hirschman J.E., Livstone M.S., Nash R., Oughtred R., Park J., Skrzypek M., Starr B., Andrada R., Binkley G., Dong Q., Hitz B.C., Miyasato S., Schroeder M., Weng S., Wong E.D., Zhu K.K., Dolinski K., Botstein D., and Cherry J.M., “Saccharomyces Genome Database,” ftp://ftp.yeastgenome.org/ yeast/, 2007.
[21] Hu, H., Yan, X., Huang, Y., Han, J. and Zhou, X.J., “Mining coherent dense subgraphs across massive biological networks for functional discovery,” Bioinformatics, Vol. 21, pp. i213-i221, 2005.
[22] Ito, T., Tashiro, K., Muta, S., Ozawa, R., Chiba, T., Nishizawa, M., Yamamoto, K., Kuhara, S. And Sakaki, Y., “Toward a protein-protein interaction map of the budding yeast: a comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 97, pp. 1143-1147, 2000.
[23] Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F. and Gerstein, M., “A Bayesian networks approach for prediction protein-protein interactions from genomic data,” Science, Vol. 302, pp. 449-453, 2003.
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[25] Li, W., Liu, Y., Huang, H.C., Peng, Y., Lin, Y., Ng, W.K. and Ong, K.L., “Dynamical systems for discovering protein complexes and functional modules from biological networks,” IEEE/ACM Transaction on Computational Biology and Bioinformatics (TCBB), to appear.
[26] Liu, H., Styles, C.A. and Fink, G.R., “Elements of the yeast pheromone response pathway required for filamentous growth of diploids,” Science, Vol. 262, pp. 1741-1744, 1993.
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[32] Tong, A.H., Drees, B., Nardell, G., Bader G.D., Brannetti, B., Castagnoli L., Evangelista, M., Ferracuti, S., Nelson, B., Paoluzi, S., Quondam, M., Zucconi, A., Hogue C.W., Fields, S., Boone, C. and Cesareni, G.,”A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules,” Science, Vol. 295, pp. 321-324, 2002.
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[38] The Yeast Protein Complex Database, http://yeast.cellzome.com/, 2007.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29703-
dc.description.abstract近來有許多高輸出的實驗可以幫我們偵測蛋白質之間的互動關係,利用這些資料,生物學家可以建立起蛋白質交互網路。如果可以從這些網路中找出蛋白質複合體將有助於我們瞭解生物的運作機制;因此,本論文提出了一個新的方法,這個方法包含了四個步驟。首先,我們計算網路中每個節點的加權連線數,並且選擇最高的那個節點當成種子;在第二個步驟,我們利用貪婪演算法找到一個密集的子網路;在第三個步驟裡,我們調整網路中連線的權重並且重新計算各個節點的加權連線數以及加權連線數的比例;在最後一個步驟,我們重複第一到第三步驟直到我們找不出任何密集的子網路為止。在我們的方法中,我們並不移除網路中任何的節點與連線,因此我們可以找出比CODENSE方法更多的重疊子網路,除此之外,實驗結果亦說明我們可以比CODENSE找到更多的蛋白質複合體。zh_TW
dc.description.abstractMany high throughput experiments have been used to detect protein interactions which can be used to a protein-protein interaction network. To recognize the protein complexes in a protein-protein interaction network can help us understand the mechanisms of the biological processes. In this thesis, we proposed a novel method with four phases to mine the protein complexes in the protein-protein interaction network. First, we calculate the weighted degree for each vertex in the network and pick the vertex with the highest weighted degree as the seed vertex. Second, we find a dense subgraph based on the greedy algorithm. Third, we modify the edge weights in the network and compute the weighted degree and the ratio of weighted degree for each vertex in the network. Finally, we repeat the above phases until no more dense subgraph can be found. Our proposed method does not remove any vertex and edge as a subgraph has been found. Therefore our method can mine more overlapping subgraphs than the CODENSE method. The experiment results show that our proposed method can find more protein complexes than the CODENSE method.en
dc.description.provenanceMade available in DSpace on 2021-06-13T01:15:29Z (GMT). No. of bitstreams: 1
ntu-96-R94725031-1.pdf: 415420 bytes, checksum: e7c753f18a4e3c931f8eb0a46c50c955 (MD5)
Previous issue date: 2007
en
dc.description.tableofcontentsTables of Contents i
List of Figures ii
List of Tables iii
Chapter 1 Introduction 1
Chapter 2 Preliminaries and Problem Definitions 4
Chapter 3 Our Proposed Method 7
3.1 The procedure of finding a dense subgraph 7
3.2 The procedure of choosing a seed vertex 7
3.3 The procedure of growing a subgraph 9
3.4 Weight modification 11
3.5 The ratio of weighted degree 12
3.6 The proposed method 13
3.7 An example 14
Chapter 4 Performance Analysis 17
4.1 Dataset and parameter settings 17
4.2 Performance evaluation for the first experiment 18
4.3 Performance evaluation for the second experiment 21
4.4 Discovering biological function modules 22
Chapter 5 Concluding Remarks 24
References 25
dc.language.isoen
dc.subject重疊的密集子網路zh_TW
dc.subject蛋白質交互網路zh_TW
dc.subject蛋白質複合體zh_TW
dc.subject貪婪演算法zh_TW
dc.subjectprotein-protein interaction networken
dc.subjectoverlapping dense subgraphen
dc.subjectgreedy algorithmen
dc.subjectprotein complexen
dc.title由蛋白質交互網路中探勘可重疊之密集子網路zh_TW
dc.titleMining Dense Overlapping Subgraphs in Weighted Protein-Protein Interaction Networksen
dc.typeThesis
dc.date.schoolyear95-2
dc.description.degree碩士
dc.contributor.oralexamcommittee呂永和,苑守慈
dc.subject.keyword蛋白質交互網路,蛋白質複合體,貪婪演算法,重疊的密集子網路,zh_TW
dc.subject.keywordprotein-protein interaction network,protein complex,greedy algorithm,overlapping dense subgraph,en
dc.relation.page29
dc.rights.note有償授權
dc.date.accepted2007-07-20
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept資訊管理學研究所zh_TW
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