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10 - Dynamic network models of protein complexes

Published online by Cambridge University Press:  05 July 2015

By
Yongjin Park  and
Joel S. Bader
Edited by
Florian Markowetz  and
Michael Boutros
Yongjin Park
Affiliation:
Johns Hopkins University
Joel S. Bader
Affiliation:
Johns Hopkins University
Florian Markowetz
Affiliation:
Cancer Research UK Cambridge Institute
Michael Boutros
Affiliation:
German Cancer Research Center, Heidelberg
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Type
Chapter
Information
Systems Genetics
Linking Genotypes and Phenotypes
 , pp. 191 - 213
Publisher: Cambridge University Press
Print publication year: 2015

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References

Airoldi, E. M., Blei, D. M., Fienberg, S. E. & Xing, E. P. (2008), ‘Mixed membership stochastic blockmodels’, Journal of Machine Learning Research 9, 1981–2014.Google Scholar
Bader, G. D. & Hogue, C. W. V. (2003), ‘An automated method for finding molecular complexes in large protein interaction networks’, BMC Bioinformatics 4, 2.CrossRefGoogle Scholar
Ball, B., Karrer, B. & Newman, M. E. J. (2011), ‘and principled method for detecting communities in networks’, Physical Review E 88, 1.Google Scholar
Bandyopadhyay, S., Mehta, M., Kuo, D., Sung, M. K., Chuang, R. et al. (2010), ‘Rewiring of genetic networks in response to DNA damage’, Science 330, 1385–1389.CrossRefGoogle Scholar
Banerjee, O., El Ghaoui, L. & d'Aspremont, A. (2008), ‘Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data’, Journal of Machine Learning Research 9, 485–516.Google Scholar
Bayati, M., Shah, D. & Sharma, M. (2006), ‘A simpler max-product maximum weight matching algorithm and the auction algorithm’, IEEE International Symposium on Information Theory 2005, 1763–1767.Google Scholar
Bayati, M., Shah, D. & Sharma, M. (2008), ‘Max-product for maximum weight matching: convergence, correctness, and LP duality’, IEEE Transactions on Information Theory 54, 1241–1251.CrossRefGoogle Scholar
Bickel, P. J. & Chen, A. (2009), ‘A nonparametric view of network models and Newman-Girvan and other modularities’, Proceedings of the National Academy of Sciences of the USA 106, 21068–21073.CrossRefGoogle Scholar
Birnbaum, K., Shasha, D., Wang, J., Jung, J., Lambert, G. et al. (2003), ‘A gene expression map of the Arabidopsis root’, Science 302, 1956–1960.CrossRefGoogle Scholar
Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003), ‘Latent Dirichlet allocation’, Journal of Machine Learning Research 3, 993–1022.Google Scholar
Clauset, A., Moore, C. & Newman, M. E. J. (2008), ‘Hierarchical structure and the prediction of missing links in networks’, Nature 453, 98–101.CrossRefGoogle Scholar
Clauset, A., Newman, M. E. J. & Moore, C. (2004), ‘Finding community structure in very large networks’, Physical Review E 70, 066111.CrossRefGoogle Scholar
Croft, D., O'Kelly, G., Wu, G., Haw, R., Gillespie, M. et al. (2011), ‘Reactome: a database of reactions, pathways and biological processes’, Nucleic Acids Research 39 (Database issue), D691–D697.CrossRefGoogle Scholar
Dittrich, M. T., Klau, G. W., Rosenwald, A., Dandekar, T. & Müller, T. (2008), ‘Identifying functional modules in protein-protein interaction networks: an integrated exact approach’, Bioinformatics 24, i223–i231.CrossRefGoogle Scholar
Friedman, J., Hastie, T. & Tibshirani, R. (2008), ‘Sparse inverse covariance estimation with the graphical lasso’, Biostatistics 9, 432–441.CrossRefGoogle Scholar
Fu, W., Song, L. & Xing, E. P. (2009), ‘Dynamic mixed membership blockmodel for evolving networks’, Proceedings of the 26th Annual International Conference on Machine Learning, pp. 329–336.Google Scholar
Geisler-Lee, J., O'Toole, N., Ammar, R., Provart, N., Millar, A. et al. (2007), ‘A predicted interactome for Arabidopsis’, Plant Physiology 145, 317–329.CrossRefGoogle Scholar
Grünwald, D., Singer, R. H. & Rout, M. (2011), ‘Nuclear export dynamics of RNA-protein complexes’. Nature 475, 333–341.CrossRefGoogle Scholar
Han, J.-D. J., Bertin, N., Hao, T., Goldberg, D. S., Berriz, G. F. et al. (2004), ‘Evidence for dynamically organized modularity in the yeast protein-protein interaction network’, Nature 430, 88–93.Google Scholar
Hastings, W. K. (1970), ‘Monte Carlo sampling methods using Markov chains and their applications’, Biometrika 57, 97–109.CrossRefGoogle Scholar
Hofman, J. M. & Wiggins, C. H. (2008), ‘Bayesian approach to network modularity’, Physical Review Letters 100, 258701.CrossRefGoogle Scholar
Jansen, R., Greenbaum, D. & Gerstein, M. (2002), ‘Relating whole-genome expression data with protein-protein interactions’, Genome Research 12, 37–46.CrossRefGoogle Scholar
Karrer, B., Levina, E. & Newman, M. E. J. (2008), ‘Robustness of community structure in networks’, Physical Review E 77, 046119.CrossRefGoogle Scholar
Kass, R. E. & Raftery, A. E. (1995), ‘Bayes factors’, Journal ofthe American Statistical Association 90, 773–795.Google Scholar
Koller, D. & Friedman, N. (2009), Probabilistic Graphical Models: Principles and Techniques, Cambridge, MA: MIT Press.Google Scholar
Komurov, K. & White, M. (2007), ‘Revealing static and dynamic modular architecture of the eukaryotic protein interaction network’, Molecular Systems Biology 3, 110.CrossRefGoogle Scholar
Lago, C., Clerici, E., Mizzi, L., Colombo, L. & Kater, M. M. (2004), ‘TBP-associated factors in Arabidopsis’, Gene 342, 231–241.CrossRefGoogle Scholar
Leskovec, J., Kleinberg, J. & Faloutsos, C. (2005), ‘Graphs over time’, Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187.Google Scholar
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdottir, H., Tamayo, P. et al. (2011), ‘Molecular signatures database (MSigDB) 3.0’, Bioinformatics 27, 1739–1740.CrossRefGoogle Scholar
Liu, Q., Greimann, J. C. & Lima, C. D. (2006), ‘Reconstitution, activities, and structure of the eukaryotic RNA exosome’, Cell 127, 1223–1237.CrossRefGoogle Scholar
Luscombe, N. M., Babu, M. M., Yu, H., Snyder, M., Teichmann, S. A. et al. (2004), ‘Genomic analysis of regulatory network dynamics reveals large topological changes’, Nature 431, 308–321.CrossRefGoogle Scholar
Metodiev, M. D., Lesko, N., Park, C. B., Cámara, Y., Shi, Y. et al. (2009), ‘Methylation of 12S rRNA is necessary for in vivo stability of the small subunit of the mammalian mitochondrial ribosome’, Cell Metabolism 9, 386–397.CrossRefGoogle Scholar
Mitchell, P., Petfalski, E., Shevchenko, A., Mann, M. & Tollervey, D. (1997), ‘The exosome: a conserved eukaryotic RNA processing complex containing multiple 3'-5' exoribonucleases’, Cell 91, 457–466.CrossRefGoogle Scholar
Palla, G., Derényi, I., Farkas, I. & Vicsek, T. (2005), ‘Uncovering the overlapping community structure of complex networks in nature and society’, Nature 435, 814–818.CrossRefGoogle Scholar
Park, Y. & Bader, J. S. (2011), ‘Resolving the structure of interactomes with hierarchical agglomerative clustering’, BMC Bioinformatics 12(Suppl. 1), S44.CrossRefGoogle Scholar
Park, Y. & Bader, J. S. (2012), ‘How networks change with time’, Bioinformatics 28, i40–i48.CrossRefGoogle Scholar
Park, Y., Moore, C. & Bader, J. S. (2010), ‘Dynamic networks from hierarchical Bayesian graph clustering’, PloS One 5, e8118.CrossRefGoogle Scholar
Pemberton, L. F., Blobel, G. & Rosenblum, J. S. (1998), ‘Transport routes through the nuclear pore complex’, Current Opinion in Cell Biology 10, 392–399.CrossRefGoogle Scholar
Petrossian, T. C. & Clarke, S. G. (2009), ‘Multiple motif scanning to identify methyltransferases from the yeast proteome’, Molecular & Cellular Proteomics 8, 1516–1526.CrossRefGoogle Scholar
Pu, S., Wong, J., Turner, B., Cho, E. & Wodak, S. J. (2009), ‘Up-to-date catalogues of yeast protein complexes’, Nucleic Acids Research 37, 825–831.CrossRefGoogle Scholar
Rabiner, L. R. (1989), A tutorial on hidden Markov models and selected applications in speech recognition', Proceedings ofthe IEEE, 77 257–286.Google Scholar
Rivera, C. G., Vakil, R. & Bader, J. S. (2010), ‘NeMo: network module identification in cytoscape’, BMC Bioinformatics 11(Suppl. 1), S61.CrossRefGoogle Scholar
Rodriguez, M. G., Leskovec, J. & Krause, A. (2010), Inferring networks of diffusion and influence', Proceedings of the 16th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, pp. 1019–1028.Google Scholar
Saveanu, C., Fromont-Racine, M., Harington, A., Ricard, F., Namane, A. et al. (2001), ‘Identification of 12 new yeast mitochondrial ribosomal proteins including 6 that have no prokaryotic homologues’, Journal of Biological Chemistry 276, 15 861–15 867.CrossRefGoogle Scholar
Schlenstedt, G., Smirnova, E., Deane, R., Solsbacher, J., Kutay, U. et al. (1997), ‘Yrb4p, a yeast ran-GTP-binding protein involved in import of ribosomal protein L25 into the nucleus’, EMBO Journal 16, 6237–6249.CrossRefGoogle Scholar
Song, L., Kolar, M. & Xing, E. P. (2009), ‘KELLER: estimating time-varying interactions between genes’, Bioinformatics 25, i128–i136.CrossRefGoogle Scholar
Stark, C., Breitkreutz, B. -J., Reguly, T., Boucher, L., Breitkreutz, A. et al. (2006), ‘BioGRID: a general repository for interaction datasets’, Nucleic Acids Research 34 (Database issue), D535–D539.CrossRefGoogle Scholar
Strambio-De-Castillia, C., Niepel, M. & Rout, M. P. (2010), ‘The nuclear pore complex: bridging nuclear transport and gene regulation’, Nature Reviews Molecular Cell Biology 11, 490–501.CrossRefGoogle Scholar
Tamada, Y., Nakamori, K., Nakatani, H., Matsuda, K., Hata, S. et al. (2007), ‘Temporary expression of the TAF10 gene and its requirement for normal development of Arabidopsis thaliana’, Plant Cell Physiology 48, 134–146.CrossRefGoogle Scholar
Tu, B. P., Kudlicki, A., Rowicka, M. & McKnight, S. L. (2005), ‘Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes’, Science 310, 1152–1158.CrossRefGoogle Scholar
Wu, Z., Irizarry, R. A., Gentleman, R., Martinez-Murillo, F. & Spencer, F. (2004), ‘A model-based background adjustment for oligonucleotide expression arrays’, Journal of the American Statistical Association 99, 909–917.CrossRefGoogle Scholar
Yedidia, J., Freeman, W. & Weiss, Y. (2005), ‘Constructing free-energy approximations and generalized belief propagation algorithms’, IEEE Transactions on Information Theory 51, 2282–2313.CrossRefGoogle Scholar
Yoo, C. J. & Wolin, S. L. (1994), ‘LA proteins from Drosophila melanogaster and Saccharomyces cerevisiae: a yeast homolog of the LA autoantigen is dispensable for growth’, Molecular and Cellular Biology 14, 5412–5424.CrossRefGoogle Scholar

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