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Classification of Low Dimensional 3-Lie Superalgebras Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Graph Centrality Based Prediction of Cancer Genes

Authors : Holger Weishaupt, Patrik Johansson, Christopher Engström, Sven Nelander, Sergei Silvestrov, Fredrik J. Swartling

Published in: Engineering Mathematics II

Publisher: Springer International Publishing

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Abstract

Current cancer therapies including surgery, radiotherapy and chemotherapy are often plagued by high failure rates. Designing more targeted and personalized treatment strategies requires a detailed understanding of druggable tumor driver genes. As a consequence, the detection of cancer driver genes has evolved to a critical scientific field integrating both high-throughput experimental screens as well as computational and statistical strategies. Among such approaches, network based prediction tools have recently been accentuated and received major focus due to their potential to model various aspects of the role of cancer genes in a biological system. In this chapter, we focus on how graph centralities obtained from biological networks have been used to predict cancer genes. Specifically, we start by discussing the current problems in cancer therapy and the reasoning behind using network based cancer gene prediction, followed by an outline of biological networks, their generation and properties. Finally, we review major concepts, recent results as well as future challenges regarding the use of graph centralities in cancer gene prediction.

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previous chapter PageRank, Connecting a Line of Nodes with a Complete Graph
next chapter Output Rate Variation Problem: Some Heuristic Paradigms and Dynamic Programming
Footnotes
1
A comprehensive list of centralities can be found in the CentiServer (http://​www.​centiserver.​org/​) [72].
 
Literature
1.
Abbott, K.L., Nyre, E.T., Abrahante, J., Ho, Y.Y., Vogel, R.I., Starr, T.K.: The candidate cancer gene database: a database of cancer driver genes from forward genetic screens in mice. Nucleic Acids Res. 43, D844–D848 (2015)CrossRef
2.
Aittokallio, T., Schwikowski, B.: Graph-based methods for analysing networks in cell biology. Brief. Bioinform. 7, 243–255 (2006)CrossRef
3.
Albert, R.: Scale-free networks in cell biology. J. Cell Sci. 118, 4947–4957 (2005)CrossRef
4.
Altay, G., Emmert-Streib, F.: Inferring the conservative causal core of gene regulatory networks. BMC Syst. Biol. 4, 1–13 (2010)CrossRef
5.
Amaral, L.A., Scala, A., Barthelemy, M., Stanley, H.E.: Classes of small-world networks. In: Proceedings of the National Academy of Sciences of the United States of America vol. 97, pp. 11149–11152 (2000)
6.
An, O., Dall’Olio, G.M., Mourikis, T.P., Ciccarelli, F.D.: NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings. Nucleic Acids Res. 44, D992–D999 (2016)CrossRef
7.
Arias, E., Kochanek, K.D., Anderson, R.N.: How does cause of death contribute to the Hispanic mortality advantage in the United States? NCHS Data Brief 221, 1–8 (2015)
8.
Ascano, M., Hafner, M., Cekan, P., Gerstberger, S., Tuschl, T.: Identification of RNA-protein interaction networks using PAR-CLIP. Wires RNA 3, 159–177 (2012)CrossRef
9.
Babu, M.M., Luscombe, N.M., Aravind, L., Gerstein, M., Teichmann, S.A.: Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol. 14, 283–291 (2004)CrossRef
10.
Balkwill, F.R., Capasso, M., Hagemann, T.: The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012)CrossRef
11.
Barabasi, A.L., Bonabeau, E.: Scale-free networks. Sci. Am. 288, 60–69 (2003)CrossRef
12.
Barabasi, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5, 101–U115 (2004)
13.
Barabasi, A.L., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011)CrossRef
14.
Beauchamp, M.A.: An improved index of centrality. Behav. Sci. 10, 161–163 (1965)CrossRef
15.
Berggard, T., Linse, S., James, P.: Methods for the detection and analysis of protein-protein interactions. Proteomics 7, 2833–2842 (2007)CrossRef
16.
Bhattacharyya, M., Chakrabarti, S.: Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies. Malar. J. 14, 70 (2015)CrossRef
17.
Björklund, A.K., Light, S., Hedin, L., Elofsson, A.: Quantitative assessment of the structural bias in protein-protein interaction assays. Proteomics 8, 4657–4667 (2008)
18.
19.
Bonacich, P.: Technique for analyzing overlapping memberships. Sociol. Methodol. 4, 176–185 (1972)CrossRef
20.
Bonacich, P., Lloyd, P.: Eigenvector-like measures of centrality for asymmetric relations. Soc. Netw. 23, 191–201 (2001)CrossRef
21.
Borgatti, S.P., Everett, M.G., Johnson, J.C.: Analyzing Social Networks. SAGE Publications Limited, Los Angeles (2013)
22.
Bossi, A., Lehner, B.: Tissue specificity and the human protein interaction network. Mol. Syst. Biol. 5, 260 (2009)CrossRef
23.
Brandes, U.: On variants of shortest-path betweenness centrality and their generic computation. Soc. Netw. 30, 136–145 (2008)CrossRef
24.
Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN 30, 107–117 (1998)CrossRef
25.
Bulyk, M.L.: Computational prediction of transcription-factor binding site locations. Genome Biol. 5, 201 (2004)CrossRef
26.
Burrell, R.A., McGranahan, N., Bartek, J., Swanton, C.: The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345 (2013)CrossRef
27.
Butts, C.T.: Social network analysis with SNA. J. Stat. Softw. 24, 1–51 (2008)
28.
Carter, S.L., Brechbuhler, C.M., Griffin, M., Bond, A.T.: Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20, 2242–2250 (2004)CrossRef
29.
Cerami, E.G., Gross, B.E., Demir, E., Rodchenkov, I., Babur, O., Anwar, N., et al.: Pathway commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011)CrossRef
30.
Chen, L., Qu, X., Cao, M., Zhou, Y., Li, W., Liang, B., et al.: Identification of breast cancer patients based on human signaling network motifs. Sci. Rep. 3, 3368 (2013)
31.
Cohen, R., Havlin, S.: Scale-free networks are ultrasmall. Phys. Rev. Lett. 90, 058701 (2003)CrossRef
32.
Coombes, R.C.: Drug testing in the patient: toward personalized cancer treatment. Sci. Transl. Med. 7 (2015)
33.
Csardi, G., Nepusz, T.: The igraph software package for complex network research. Int. J. Complex Syst. 1695, 1–9 (2006)
34.
Dangalchev, C.: Residual closeness in networks. Phys. A 365, 556–564 (2006)CrossRef
35.
del Rio, G., Koschützki, D., Coello, G.: How to identify essential genes from molecular networks? BMC Syst. Biol. 3, 1–12 (2009)
36.
Diamandis, M., White, N.M.A., Yousef, G.M.: Personalized medicine: marking a new epoch in cancer patient management. Mol. Cancer Res. 8, 1175–1187 (2010)CrossRef
37.
Dobrin, R., Beg, Q.K., Barabasi, A.L., Oltvai, Z.N.: Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinform. 5, 1–8 (2004)CrossRef
38.
39.
Eirew, P., Steif, A., Khattra, J., Ha, G., Yap, D., Farahani, H., et al.: Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015)CrossRef
40.
Estrada, E.: Virtual identification of essential proteins within the protein interaction network of yeast. Proteomics 6, 35–40 (2006)CrossRef
41.
Estrada, E.: Protein bipartivity and essentiality in the yeast protein-protein interaction network. J. Proteome Res. 5, 2177–2184 (2006)CrossRef
42.
Euskirchen, G.M., Rozowsky, J.S., Wei, C.L., Lee, W.H., Zhang, Z.D.D., Hartman, S., et al.: Mapping of transcription factor binding regions in mammalian cells by ChIP: comparison of array- and sequencing-based technologies. Genome Res. 17, 898–909 (2007)CrossRef
43.
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., et al.: The reactome pathway knowledgebase. Nucleic Acids Res. 44, D481–D487 (2016)CrossRef
44.
Faith, J.J., Hayete, B., Thaden, J.T., Mogno, I., Wierzbowski, J., Cottarel, G., et al.: Large- scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5, 54–66 (2007)CrossRef
45.
Forbes, S.A., Beare, D., Gunasekaran, P., Leung, K., Bindal, N., Boutselakis, H., et al.: COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015)CrossRef
46.
Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)CrossRef
47.
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1, 215–239 (1979)CrossRef
48.
Freeman, L.C., Borgatti, S.P., White, D.R.: Centrality in valued graphs - a measure of betweenness based on network flow. Soc. Netw. 13, 141–154 (1991)MathSciNetCrossRef
49.
Fronza, R., Vasciaveo, A., Benso, A., Schmidt, M.: A graph based framework to model virus integration sites. Comput. Struct. Biotechnol. J. 14, 69–77 (2016)CrossRef
50.
Giam, M., Rancati, G.: Aneuploidy and chromosomal instability in cancer: a jackpot to chaos. Cell Div. 10, 3 (2015)CrossRef
51.
Gillis, J., Ballouz, S., Pavlidis, P.: Bias tradeoffs in the creation and analysis of protein- protein interaction networks. J. Proteomics 100, 44–54 (2014)CrossRef
52.
Giot, L., Bader, J.S., Brouwer, C., Chaudhuri, A., Kuang, B., Li, Y., et al.: A protein interaction map of Drosophila melanogaster. Science 302, 1727–1736 (2003)CrossRef
53.
Gleich, D.F.: Chapter 7 on MatlabBGL. Models and Algorithms for PageRank Sensitivity. Stanford University (2009)
54.
Goh, K.I., Kahng, B., Kim, D.: Universal behavior of load distribution in scale-free networks. Phys. Rev. Lett. 87, 278701 (2001)CrossRef
55.
Goh, K.I., Cusick, M.E., Valle, D., Childs, B., Vidal, M., Barabasi, A.L.: The human disease network. In: Proceedings of the National Academy of Sciences of the United States of America vol. 104, pp. 8685–8690 (2007)
56.
Gonzalez-Perez, A., Perez-Llamas, C., Deu-Pons, J., Tamborero, D., Schroeder, M.P., Jene-Sanz, A., et al.: IntOGen-mutations identifies cancer drivers across tumor types. Nat. Methods 10, 1081–1082 (2013)CrossRef
57.
Grassler, J., Koschützki, D., Schreiber, F.: CentiLib: comprehensive analysis and exploration of network centralities. Bioinformatics 28, 1178–1179 (2012)
58.
Gu, Z.G., Wang, J.: CePa: an R package for finding significant pathways weighted by multiple network centralities. Bioinformatics 29, 658–660 (2013)
59.
Guan, Y.F., Gorenshteyn, D., Burmeister, M., Wong, A.K., Schimenti, J.C., Handel, M.A., et al.: Tissue- specific functional networks for prioritizing phenotype and disease genes. PLoS Comput. Biol. 8, e1002694 (2012)CrossRef
60.
Guelzim, N., Bottani, S., Bourgine, P., Kepes, F.: Topological and causal structure of the yeast transcriptional regulatory network. Nat. Genet. 31, 60–63 (2002)CrossRef
61.
Guinney, J., Dienstmann, R., Wang, X., de Reynies, A., Schlicker, A., Soneson, C., et al.: The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015)CrossRef
62.
Hage, P., Harary, F.: Eccentricity and centrality in networks. Soc. Netw. 17, 57–63 (1995)CrossRef
63.
Hahn, M.W., Kern, A.D.: Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol. Biol. Evol. 22, 803–806 (2005)CrossRef
64.
Hardy, S., Legagneux, V., Audic, Y., Paillard, L.: Reverse genetics in eukaryotes. Biol. Cell 102, 561–580 (2010)CrossRef
65.
Haury, A.C., Mordelet, F., Vera-Licona, P., Vert, J.P.: TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst. Biol. 6, 1–17 (2012)CrossRef
66.
Hirschhorn, J.N., Daly, M.J.: Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95–108 (2005)CrossRef
67.
Hu, P.Z., Bader, G., Wigle, D.A., Emili, A.: Computational prediction of cancer-gene function. Nat. Rev. Cancer 7, 23–34 (2007)CrossRef
68.
Huang, X., Zi, Z.K.: Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR). Mol. Biosyst. 10, 2023–2030 (2014)CrossRef
69.
Huynh-Thu, V.A., Irrthum, A., Wehenkel, L., Geurts, P.: Inferring regulatory networks from expression data using tree-based methods. Plos One 5, e12776 (2010)CrossRef
70.
Izudheen, S., Mathew, S.: Cancer gene identification using graph centrality. Curr. Sci. 105, 1143–1148 (2013)
71.
Jalali, S., Bhartiya, D., Lalwani, M.K., Sivasubbu, S., Scaria, V.: Systematic transcriptome wide analysis of lncRNA-miRNA interactions. Plos One 8, e53823 (2013)CrossRef
72.
Jalili, M., Salehzadeh-Yazdi, A., Asgari, Y., Arab, S.S., Yaghmaie, M., Ghavamzadeh, A., et al.: CentiServer: a comprehensive resource, web-based application and R package for centrality analysis. Plos One 10, e0143111 (2015)CrossRef
73.
Jensen, L.J., Kuhn, M., Stark, M., Chaffron, S., Creevey, C., Muller, J., et al.: STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37, D412–D416 (2009)CrossRef
74.
Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabasi, A.L.: The large-scale organization of metabolic networks. Nature 407, 651–654 (2000)CrossRef
75.
Jeong, H., Mason, S.P., Barabasi, A.L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411, 41–42 (2001)CrossRef
76.
Jinq, Z., Hong, Y., Jianhua, L., Cao, Z.W., Li, Y.X.: Complex networks theory for analyzing metabolic networks. Chin. Sci. Bull. 51, 1529–1537 (2006)MathSciNetCrossRef
77.
Jonsson, P.F., Bates, P.A.: Global topological features of cancer proteins in the human interactome. Bioinformatics 22, 2291–2297 (2006)CrossRef
78.
Joy, M.P., Brock, A., Ingber, D.E., Huang, S.: High-betweenness proteins in the yeast protein interaction network. J. Biomed. Biotechnol. 2005, 96–103 (2005)CrossRef
79.
Junker, B.H., Koschützki, D., Schreiber, F.: Exploration of biological network centralities with CentiBiN. BMC Bioinform. 7, 1–7 (2006)
80.
Kamburov, A., Stelzl, U., Lehrach, H., Herwig, R.: The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res. 41, D793–D800 (2013)CrossRef
81.
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., Tanabe, M.: KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016)CrossRef
82.
Kandoth, C., McLellan, M.D., Vandin, F., Ye, K., Niu, B., Lu, C., et al.: Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013)CrossRef
83.
Karabekmez, M.E., Kirdar, B.: A novel topological centrality measure capturing biologically important proteins. Mol. Biosyst. 12, 666–673 (2016)CrossRef
84.
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18, 39–43 (1953)MATHCrossRef
85.
Kerrien, S., Aranda, B., Breuza, L., Bridge, A., Broackes-Carter, F., Chen, C., et al.: The IntAct molecular interaction database in 2012. Nucleic Acids Res. 40, D841–D846 (2012)CrossRef
86.
Kim, S.Y., Volsky, D.J.: PAGE: parametric analysis of gene set enrichment. BMC Bioinform. 6, 144 (2005)CrossRef
87.
Kim, W.: Prediction of essential proteins using topological properties in GO-pruned PPI network based on machine learning methods. Tsinghua Sci. Technol. 17, 645–658 (2012)CrossRef
88.
Kool, J., Berns, A.: High throughput insertional mutagenesis screens in mice to identify oncogenic networks (vol 9, pg 389, 2009). Nat. Rev. Cancer 9, 604–604 (2009)CrossRef
89.
Koschützki, D., Schreiber, F.: Comparison of centralities for biological networks. In: German Conference on Bioinformatics (2004)
90.
Koschützki, D., Schreiber, F.: Centrality analysis methods for biological networks and their application to gene regulatory networks. Gene Regul. Syst. Biol. 2, 193–201 (2008)
91.
Koschützki, D., Schwobbermeyer, H., Schreiber, F.: Ranking of network elements based on functional substructures. J. Theor. Biol. 248, 471–479 (2007)
92.
Kreso, A., O’Brien, C.A., van Galen, P., Gan, O.I., Notta, F., Brown, A.M.K., et al.: Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science 339, 543–548 (2013)CrossRef
93.
Li, J.H., Liu, S., Zhou, H., Qu, L.H., Yang, J.H.: StarBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 42, D92–D97 (2014)CrossRef
94.
Li, M., Zhang, H., Wang, J.X., Pan, Y.: A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC Syst. Biol. 6, 15 (2012)CrossRef
95.
Li, S.M., Armstrong, C.M., Bertin, N., Ge, H., Milstein, S., Boxem, M., et al.: A map of the interactome network of the metazoan C-elegans. Science 303, 540–543 (2004)CrossRef
96.
Lin, N.: Foundations of Social Research. McGraw-Hill, New York (1976)
97.
Linghu, B., Snitkin, E.S., Hu, Z., Xia, Y., Delisi, C.: Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol. 10, R91 (2009)CrossRef
98.
Linghu, B., Franzosa, E.A., Xia, Y.: Construction of functional linkage gene networks by data integration. Methods Mol. Biol. 939, 215–232 (2013)CrossRef
99.
Luscombe, N.M., Babu, M.M., Yu, H.Y., Snyder, M., Teichmann, S.A., Gerstein, M.: Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004)CrossRef
100.
Ma, X.K., Gao, L.: Biological network analysis: insights into structure and functions. Brief. Funct. Genomics 11, 434–442 (2012)CrossRef
101.
Magger, O., Waldman, Y.Y., Ruppin, E., Sharan, R.: Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLoS Comput. Biol. 8, e1002690 (2012)CrossRef
102.
Marbach, D., Costello, J.C., Kuffner, R., Vega, N.M., Prill, R.J., Camacho, D.M., et al.: Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012)CrossRef
103.
March, H.N., Rust, A.G., Wright, N.A., Ten Hoeve, J., de Ridder, J., Eldridge, M., et al.: Insertional mutagenesis identifies multiple networks of cooperating genes driving intestinal tumorigenesis. Nat Genet 43, 1202–U1255 (2011)
104.
Margolin, A.A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., et al.: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7, 1–15 (2006)CrossRef
105.
Marusyk, A., Polyak, K.: Tumor heterogeneity: causes and consequences. BBA-Rev. Cancer 1805, 105–117 (2010)
106.
Mathelier, A., Wasserman, W.W.: The next generation of transcription factor binding site prediction. PLoS Comput. Biol. 9, e1003214 (2013)CrossRef
107.
Matys, V., Kel-Margoulis, O.V., Fricke, E., Liebich, I., Land, S., Barre-Dirrie, A., et al.: TRANSFAC (R) and its module TRANSCompel (R): transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34, D108–D110 (2006)CrossRef
108.
McGranahan, N., Swanton, C.: Biological and therapeutic impact of intratumor heterogeneity in cancer evolution (vol 27, pg 15, 2015). Cancer Cell 28, 141–141 (2015)CrossRef
109.
Meacham, C.E., Morrison, S.J.: Tumour heterogeneity and cancer cell plasticity. Nature 501, 328–337 (2013)CrossRef
110.
Meyer, M., Reimand, J., Lan, X., Head, R., Zhu, X., Kushida, M., et al.: Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 112, pp. 851–856 (2015)
111.
Miernyk, J.A., Thelen, J.J.: Biochemical approaches for discovering protein-protein interactions. Plant J. 53, 597–609 (2008)CrossRef
112.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002)CrossRef
113.
Moreau, Y., Tranchevent, L.C.: Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat. Rev. Genet. 13, 523–536 (2012)CrossRef
114.
Moresco, E.M.Y., Li, X.H., Beutler, B.: Going forward with genetics recent technological advances and forward genetics in mice. Am. J. Pathol. 182, 1462–1473 (2013)CrossRef
115.
Morrissy, A.S., Garzia, L., Shih, D.J.H., Zuyderduyn, S., Huang, X., Skowron, P., et al.: Divergent clonal selection dominates medulloblastoma at recurrence. Nature 529, 351–357 (2016)CrossRef
116.
Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E 64, 016132 (2001)CrossRef
117.
Ortutay, C., Vihinen, M.: Identification of candidate disease genes by integrating gene ontologies and protein-interaction networks: case study of primary immunodeficiencies. Nucleic Acids Res. 37, 622–628 (2009)CrossRef
118.
Oti, M., Snel, B., Huynen, M.A., Brunner, H.G.: Predicting disease genes using protein-protein interactions. J. Med. Genet. 43, 691–698 (2006)CrossRef
119.
Özgür, A., Vu, T., Erkan, G., Radev, D.R.: Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics 24, I277–I285 (2008)
120.
Patel, A.P., Tirosh, I., Trombetta, J.J., Shalek, A.K., Gillespie, S.M., Wakimoto, H., et al.: Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014)CrossRef
121.
Phizicky, E.M., Fields, S.: Protein-protein interactions - Methods for detection and analysis. Microbiol. Rev. 59, 94–123 (1995)
122.
Poulin, R., Boily, M.C., Masse, B.R.: Dynamical systems to define centrality in social networks. Soc. Netw. 22, 187–220 (2000)CrossRef
123.
Prasad, T.S.K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., et al.: Human protein reference database-2009 update. Nucleic Acids Res. 37, D767–D772 (2009)CrossRef
124.
Price, A.L., Spencer, C.C.A., Donnelly, P.: Progress and promise in understanding the genetic basis of common diseases. Proc. R. Soc. B-Biol. Sci. 282, 20151684 (2015)
125.
Ptacek, J., Devgan, G., Michaud, G., Zhu, H., Zhu, X.W., Fasolo, J., et al.: Global analysis of protein phosphorylation in yeast. Nature 438, 679–684 (2005)CrossRef
126.
Qin, J., Hu, Y.H., Xu, F., Yalamanchili, H.K., Wang, J.W.: Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods. Methods 67, 294–303 (2014)CrossRef
127.
Rajasingh, I., Rajan, B., Florence, I.D.: Betweeness-centrality of grid networks. In: Proceedings of the 2009 International Conference on Computer Technology and Development, vol. 1, pp. 407–410 (2009)
128.
Ramanan, V.K., Shen, L., Moore, J.H., Saykin, A.J.: Pathway analysis of genomic data: concepts, methods, and prospects for future development. Trends Genet. 28, 323–332 (2012)CrossRef
129.
Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabasi, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)CrossRef
130.
Resendis-Antonio, O., Freyre-Gonzalez, J.A., Menchaca-Mendez, R., Gutierrez-Rios, R.M., Martinez- Antonio, A., Avila-Sanchez, C., et al.: Modular analysis of the transcriptional regulatory network of E-coli. Trends Genet. 21, 16–20 (2005)CrossRef
131.
Risch, N.J.: Searching for genetic determinants in the new millennium. Nature 405, 847–856 (2000)CrossRef
132.
Rives, A.W., Galitski, T.: Modular organization of cellular networks. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 100, pp. 1128–1133 (2003)
133.
Rochat, Y.: Closeness centrality extended to unconnected graphs: the harmonic centrality index. ASNA. No. EPFL-CONF-200525 (2009)
134.
Ruhnau, B.: Eigenvector-centrality - a node-centrality? Soc. Netw. 22, 357–365 (2000)CrossRef
135.
136.
Salwinski, L., Miller, C.S., Smith, A.J., Pettit, F.K., Bowie, J.U., Eisenberg, D.: The database of interacting proteins: 2004 update. Nucleic Acids Res. 32, D449–D451 (2004)CrossRef
137.
Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W.W., Lenhard, B.: JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 32, D91–D94 (2004)CrossRef
138.
Sander, J.D., Joung, J.K.: CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347–355 (2014)CrossRef
139.
Sanz, J., Navarro, J., Arbues, A., Martin, C., Marijuan, P.C., Moreno, Y.: The transcriptional regulatory network of Mycobacterium tuberculosis. Plos One 6, e22178 (2011)CrossRef
140.
Scardoni, G., Petterlini, M., Laudanna, C.: Analyzing biological network parameters with CentiScaPe. Bioinformatics 25, 2857–2859 (2009)CrossRef
141.
Schadt, E.E.: Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009)CrossRef
142.
Schoch, D., Brandes, U.: Centrality as a predictor of lethal proteins: performance and robustness. In: MMB & DFT (2014)
143.
Sharma, A., Gulbahce, N., Pevzner, S.J., Menche, J., Ladenvall, C., Folkersen, L., et al.: Network-based analysis of genome wide association data provides novel candidate genes for lipid and lipoprotein traits. Mol. Cell. Proteomics 12, 3398–3408 (2013)CrossRef
144.
Shen-Orr, S.S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31, 64–68 (2002)CrossRef
145.
146.
Shoemaker, B.A., Panchenko, A.R.: Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLoS Comput. Biol. 3, 595–601 (2007)
147.
Siddani, B.R., Pochineni, L.P., Palanisamy, M.: Candidate gene identification for systemic lupus erythematosus using network centrality measures and gene ontology. Plos One 8, e81766 (2013)CrossRef
148.
Siddharthan, R.: Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix. Plos One 5, e9722 (2010)CrossRef
149.
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2015. CA: Cancer J. Clin. 65, 5–29 (2015)
150.
Silva, J., Chang, K., Hannon, G.J., Rivas, F.V.: RNA-interference-based functional genomics in mammalian cells: reverse genetics coming of age. Oncogene 23, 8401–8409 (2004)CrossRef
151.
Simoes, R.D., Emmert-Streib, F.: Bagging statistical network inference from large-scale gene expression data. Plos One 7, e33624 (2012)CrossRef
152.
153.
Sorlie, T., Tibshirani, R., Parker, J., Hastie, T., Marron, J.S., Nobel, A., et al.: Repeated observation of breast tumor subtypes in independent gene expression data sets. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 100, pp. 8418–8423 (2003)
154.
Sottoriva, A., Spiteri, I., Piccirillo, S.G., Touloumis, A., Collins, V.P., Marioni, J.C., et al.: Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 110, pp. 4009–4014 (2013)
155.
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 100, pp. 12123–12128 (2003)
156.
Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006)CrossRef
157.
Stelzl, U., Worm, U., Lalowski, M., Haenig, C., Brembeck, F.H., Goehler, H., et al.: A human protein- protein interaction network: a resource for annotating the proteome. Cell 122, 957–968 (2005)CrossRef
158.
Stuart, J.M., Segal, E., Koller, D., Kim, S.K.: A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003)CrossRef
159.
Sturm, D., Witt, H., Hovestadt, V., Khuong-Quang, D.A., Jones, D.T., Konermann, C., et al.: Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22, 425–437 (2012)CrossRef
160.
Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P.L., Ideker, T.: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431–432 (2011)CrossRef
161.
Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 102, pp. 15545–15550 (2005)
162.
Szalay, K.Z., Csermely, P.: Perturbation centrality and turbine: a novel centrality measure obtained using a versatile network dynamics tool. Plos One 8, e78059 (2013)CrossRef
163.
Tang, X., Wang, J., Zhong, J., Pan, Y.: Predicting essential proteins based on weighted degree centrality. IEEE/ACM Trans. Comput. Biol. Bioinform./IEEE ACM 11, 407–418 (2014)CrossRef
164.
Tarca, A.L., Draghici, S., Bhatti, G., Romero, R.: Down-weighting overlapping genes improves gene set analysis. BMC Bioinform. 13, 136 (2012)CrossRef
165.
Taylor, M.D., Northcott, P.A., Korshunov, A., Remke, M., Cho, Y.J., Clifford, S.C., et al.: Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol. 123, 465–472 (2012)CrossRef
166.
Tong, A.H.Y., Evangelista, M., Parsons, A.B., Xu, H., Bader, G.D., Page, N., et al.: Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001)CrossRef
167.
Tong, A.H.Y., Lesage, G., Bader, G.D., Ding, H.M., Xu, H., Xin, X.F., et al.: Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004)CrossRef
168.
Tschida, B.R., Largaespada, D.A., Keng, V.W.: Mouse models of cancer: sleeping beauty transposons for insertional mutagenesis screens and reverse genetic studies. Semin. Cell Dev. Biol. 27, 86–95 (2014)CrossRef
169.
Uren, A.G., Kool, J., Berns, A., van Lohuizen, M.: Retroviral insertional mutagenesis: past, present and future. Oncogene 24, 7656–7672 (2005)CrossRef
170.
Uren, A.G., Kool, J., Matentzoglu, K., de Ridder, J., Mattison, J., van Uitert, M., et al.: Large-scale mutagenesis in p19ARF-and p53-deficient mice identifies cancer genes and their collaborative networks. Cell 133, 727–741 (2008)CrossRef
171.
Valencia, A., Pazos, F.: Computational methods for the prediction of protein interactions. Curr. Opin. Struct. Biol. 12, 368–373 (2002)CrossRef
172.
Valente, T.W., Foreman, R.K.: Integration and radiality: measuring the extent of an individual’s connectedness and reachability in a network. Soc. Netw. 20, 89–105 (1998)CrossRef
173.
Valouev, A., Johnson, D.S., Sundquist, A., Medina, C., Anton, E., Batzoglou, S., et al.: Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat. Methods 5, 829–834 (2008)CrossRef
174.
Verhaak, R.G., Hoadley, K.A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M.D., et al.: Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010)
175.
Vidal, M., Cusick, M.E., Barabasi, A.L.: Interactome networks and human disease. Cell 144, 986–998 (2011)CrossRef
176.
Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz Jr., L.A., Kinzler, K.W.: Cancer genome landscapes. Science 339, 1546–1558 (2013)CrossRef
177.
von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S., et al.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403 (2002)CrossRef
178.
Wachi, S., Yoneda, K., Wu, R.: Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics 21, 4205–4208 (2005)CrossRef
179.
Wagner, A., Fell, D.A.: The small world inside large metabolic networks. Proc. R. Soc. B-Biol. Sci. 268, 1803–1810 (2001)CrossRef
180.
Wang, J., Chen, G., Li, M., Pan, Y.: Integration of breast cancer gene signatures based on graph centrality. BMC Syst. Biol. 5(Suppl 3), S10 (2011)CrossRef
181.
Wang, K., Li, M.Y., Hakonarson, H.: Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet. 11, 843–854 (2010)CrossRef
182.
Wang, P.W., Qin, J., Qin, Y.M., Zhu, Y., Wang, L.L.Y., Li, M.L.J., et al.: ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks. Nucleic Acids Res. 43, W264–W269 (2015)CrossRef
183.
Wang, S., Sun, H.F., Ma, J., Zang, C.Z., Wang, C.F., Wang, J., et al.: Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat. Protoc. 8, 2502–2515 (2013)CrossRef
184.
Wang, W.Y.S., Barratt, B.J., Clayton, D.G., Todd, J.A.: Genome-wide association studies: theoretical and practical concerns. Nat. Rev. Genet. 6, 109–118 (2005)CrossRef
185.
Wang, X., Gulbahce, N., Yu, H.: Network-based methods for human disease gene prediction. Brief. Funct. Genomics 10, 280–293 (2011)CrossRef
186.
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)CrossRef
187.
Wei, W., Pfeffer, J., Reminga, J., Carley, K.M.: Handling Weighted, Asymmetric, Self- Looped, and Disconnected Networks in ORA (No. CMU-ISR-11-113). Carnegie Mellon University, Pittsburgh (2011)
188.
Weishaupt, H., Johansson, P., Engström, C., Nelander, S., Silvestrov, S., Swartling, FJ.: Loss of conservation of graph centralities in reverse-engineered transcriptional regulatory networks. In: 16th Applied Stochastic Models and Data Analysis International Conference (ASMDA2015) with Demographics 2015 Workshop (2015)
189.
Wu, X.B., Li, S.: Cancer Gene Prediction Using a Network Approach. Chapman & Hall/CRC Mathematical and Computational Biology, pp. 191–212 (2010)
190.
Wuchty, S.: Scale-free behavior in protein domain networks. Mol. Biol. Evol. 18, 1694–1702 (2001)CrossRef
191.
Wuchty, S., Almaas, E.: Evolutionary cores of domain co-occurrence networks. BMC Evol. Biol. 5, 1–12 (2005)CrossRef
192.
193.
Wuchty, S., Oltvai, Z.N., Barabasi, A.L.: Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat. Genet. 35, 176–179 (2003)CrossRef
194.
Xu, J.Z., Li, Y.J.: Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22, 2800–2805 (2006)CrossRef
195.
Yeger-Lotem, E., Sattath, S., Kashtan, N., Itzkovitz, S., Milo, R., Pinter, R.Y., et al.: Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 101, pp. 5934–5939 (2004)
196.
Yook, S.H., Oltvai, Z.N., Barabasi, A.L.: Functional and topological characterization of protein interaction networks. Proteomics 4, 928–942 (2004)CrossRef
197.
Zellmer, V.R., Zhang, S.Y.: Evolving concepts of tumor heterogeneity. Cell Biosci. 4, 1–8 (2014)CrossRef
198.
Zhang, L.V., King, O.D., Wong, S.L., Goldberg, D.S., Tong, A.H., Lesage, G., et al.: Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. J. Biol. 4, 6 (2005)CrossRef
199.
Zhang, M., Deng, J., Fang, C.V., Zhang, X., Lu, L.J.: Molecular network analysis and applications. In: Alterovitz, G., Ramoni, M. (eds.) Knowledge-Based Bioinformatics: From Analysis to Interpretation, pp. 253. Wiley, Chichester (2011)
200.
Zhao, B.Y., Pritchard, J.R., Lauffenburger, D.A., Hemann, M.T.: Addressing genetic tumor heterogeneity through computationally predictive combination therapy. Cancer Discov. 4, 166–174 (2014)CrossRef
201.
Zhao, H., Liu, T., Liu, L., Zhang, G., Pang, L., Yu, F., et al.: Chromatin states modify network motifs contributing to cell-specific functions. Sci. Rep. 5, 11938 (2015)CrossRef
202.
Zhao, W., Langfelder, P., Fuller, T., Dong, J., Li, A., Hovarth, S.: Weighted gene coexpression network analysis: state of the art. J. Biopharm. Stat. 20, 281–300 (2010)MathSciNetCrossRef
203.
Zhu, C., Wu, C., Aronow, B.J., Jegga, A.G.: Computational approaches for human disease gene prediction and ranking. Adv. Exp. Med. Biol. 799, 69–84 (2014)CrossRef
204.
Zhu, X., Gerstein, M., Snyder, M.: Getting connected: analysis and principles of biological networks. Genes Dev. 21, 1010–1024 (2007)CrossRef
Metadata
Title
Graph Centrality Based Prediction of Cancer Genes
Authors
Holger Weishaupt
Patrik Johansson
Christopher Engström
Sven Nelander
Sergei Silvestrov
Fredrik J. Swartling
Copyright Year
2016
Book
Engineering Mathematics II

Print ISBN: 978-3-319-42104-9

Electronic ISBN: 978-3-319-42105-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-42105-6_13

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