Manifold learning in protein interactomes

Journal of Computational Biology

Volumn 18, Issue 1, 2011, Pages 81-96

  Marras, Elisabetta ^a   Travaglione, Antonella ^a   Capobianco, Enrico ^a  

^a CENTER FOR ADVANCED STUDIES   (Italy)

Author keywords

biological validation; embedding and recovery; protein interactomes; subspace decomposition

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; COMPUTER SIMULATION; HUMAN; PROTEIN ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; MODELS, BIOLOGICAL; PROTEIN INTERACTION MAPPING;

EID: 78651340020     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2009.0258     Document Type: Article

References (51)

1. Bader, J.S., Chaudhuri, A., Rothberg, J.M., et al. 2003. Gaining confidence in high-throughput protein interaction networks. Nat. Biotech. 22, 78-85.
2. Barbano, P.E., Spivak, M., Flajolet, M., et al. 2007. A mathematical tool for exploring the dynamics of biological networks. Proc. Natl. Acad. Sci. USA 104, 19169-19174.
3. Cardoso, J. 1989. Source separation using higher order moments. Proc. ICASSP 2109-2112.
4. Cardoso, J., and Soulomiac, A.S. 1993. Blind beamforming for non-Gaussian signals. Proc. IEE-F 140, 362-370.
5. Clarke, R., Ressom, H.W., Wang, A., et al. 2008. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat. Rev. Cancer 8, 37-49.
6. Clauset, A., Newman, M.E.J., and Moore, C. 2004. Finding community structure in very large networks. Phy. Rev. E 70, 066111.
7. Clauset, A., Shalizi, C.R., and Newman, M.E.J. 2009. Power-law distributions in empirical data. SIAM Rev. 51, 661-703.
8. Comon, P. 1994. Independent component analysis-a new concept? Signal Proc. 36, 287-314.
9. Diaconis, P., and Freedman, D. 1984. Asymptotics of graphical projection pursuit. Ann. Statist. 12, 793-815.
10. Deane, C.M., Salwinski, L., Xenarios, I., et al. 2002. Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol. Cell. Proteom. 1, 349-356.
11. Donoho, D.L. 2000. High-dimensional data analysis: the curses and blessings of dimensionality. Presented at the AMS Conference on Math Challenges of the 21st Century.
12. Edwards, A.M., Kus, B., Jansen, R., et al. 2002. Bridging structural biology and genomics: assessing protein interaction data with known complexes. Trends Genet. 18, 529-536.
13. Forman, J.J., Clemons, P.A., Schreiber, S.L., et al. 2005. SpectralNET an application for spectral graph analysis and visualization. BMC Bioinform. 6, 260.
14. Fortunato, S., and Barthelemy, M. 2007. Resolution limit in community detection. Proc. Natl. Acad. Sci. USA 104, 36-41.
15. Gavin, A.C., Aloy, P., Grandi, P., et al. 2006. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631-636.
16. GO. 2010. Available at: http://go.princeton.edu/cgi-bin/GOTermMapper. Accessed June 1, 2010.
17. Gorban, A., Kegl, B., Wunsch, D., et al. 2007. Principal manifolds for data visualisation and dimension reduction. Lect. Notes Comput. Sci. 58.
18. Han, J.D.J., Bertin, N., Hao, T., et al. 2004. Evidence for dynamically organized modularity in the yeast proteinprotein interaction network. Nature 430, 88-93.
19. Hart, G.T., Ramani, A.K., and Marcotte, E.M. 2006. How complete are current yeast and human protein-interaction networks? Gen. Biol. 7, 120.1-120.9.
20. Hartwell, L.H., Hopfield, J.J., Leibler, S., et al. 1999. From molecular to modular cell biology. Nature 402, 47-52.
21. Hofman, J.M., and Wiggins, C.H. 2008. Bayesian approach to network modularity. Phys. Rev Lett. 100, 258701.
22. Hyvarinen, A., and Oja, E. 1997. A fast fixed-point algorithm for independent component analysis. Neur. Comput. 9, 1483-1492. (Pubitemid 127462801)
23. Hyvarinen, A. 1999. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neur. Netw. 10, 626-634.
24. Hyvrinen, A., Karhunen, J., and Oja, E. 2001. Independent Component Analysis. Wiley, New York.
25. Ito, T., Chiba, T., Ozawa, R., et al. 2001. A comprehensive two-hybrid analysis to explore the yeast protein interctome. Proc. Natl. Acad. Sci. USA 98, 4569-4574.
26. Ivakhno, S., and Armstrong, J.D. 2007. Nonlinear dimensionality reduction of signaling networks. BMC Syst. Biol. 1, 27.
27. Jeong, H., Mason, S.P., Barabasi, A.L., et al. 2001. Lethality and centrality in protein networks. Nature 411, 1-42.
28. Jonsson, P.F., and Bates, P.A. 2006. Global topological features of cancer proteins in the human interactome. Bioinformatics 22, 2291-2297.
29. Kitsak, M., Havlin, S., Paul, G., et al. 2007. Betweenness centrality of fractal and non-fractal scale-free model networks and tests on real networks. Phys. Rev. E 75, 056115.
30. Krogan, N.J., Cagney, G., Yu, H., et al. 2006. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637-643.
31. Learned-Miller, E., and Fisher, III, J.W. 2003. ICA using spacings estimates of entropy. J. Mach. Learn. Res. 4, 1271-1295.
32. de Lichtenberg, U., Jensen, L.J., Brunak, S., et al. 2005. Dynamic complex formation during the yeast cell cycle. Science 307, 724-727.
33. Lu, L.J., Xia, Y., Paccanaro, A., et al. 2005. Assessing the limits of genomic data integration for predicting protein networks. Gen. Res. 15, 945-953.
34. Marras, E., and Capobianco, E. 2008. Advances in human protein interactome inference, 89-94. In Dabo-Niang, S., and Ferraty, F., eds. Functional and Operational Statistics. Physica-Verlag, Heidelberg.
35. Marras E., and Capobianco E. 2009. Mining protein-protein interaction networks: denoising effects. J. Stat. Mech. Th. Exp. P01006. MIPS. 2010. Available at: http://mips.helmholtzmuenchen.de/genre/proj/yeast/Search/Catalogs/ catalog.jsp. Accessed June 1, 2010.
36. Newman, M.E.J., and Girvan, M. 2004. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113.
37. Newman, M.E.J. 2006. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103, 8577-8582.
38. Nilsson, J., Fioretos, T., Hoglund, M., et al. 2004. Approximate geodesic distances reveal biologically relevant structures in microarray data. Bioinformatics 20, 874-880.
39. Palla, G., Dernyi, I., Farkas, I., et al. 2005. Uncovering the overlapping community structure of complex networks in nature an society. Nature 435, 814-818.
40. Reguly, T., Breitkreutz, A., Boucher, L., et al. 2006. A comprehensive curation and analysis of global interaction networks in S. cerevisiae. J. Biol. 5, 11.
41. Roswall, M., and Bergstrom, C.T. 2007. An information theoretic framework for resolving community structure in comllex networks. Proc. Natl. Acad. Sci. USA 104, 7327-7331.
42. Rual, J.F., Venkatesan, K., Hao, T., et al. 2005. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173-1178.
43. Schlkopf, B., Smola, A.J., and Muller, K.R. 1999. Kernel principal component analysis, 327-352. In Schlkopf, B., Burges, C.J.C., Smola, A.J., eds. Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge, MA.
44. Tenenbaum, J.B., de Silva, V., and Langford, J.C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323.
45. Troyanskaya, O.G., Dolinski, K., Owen, A.B., et al. 2003. A Bayesian framework for combining heterogeneous data sources for gene function prediction in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA 100, 8348-8353.
46. Uetz, P., Giot, L., Cagney, G., et al. 2000. Comprehensive analysis of protein-protein interactions in accharomyces cerevisiae. Nature 403, 623-627.
47. Ulitsky, I., Karp, R.M., and Shamir, R. 2008. Detecting disease-specific disregulated pathways via analysis of clinical expression profiles. Proc. RECOMB 2008 4955.
48. Vidal, M. 2005. Interactome modeling. FEBS Lett. 579, 1834-1838.
49. von Mering, C., Krause, R., Snel, B., et al. 2002. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399-401.
50. Wachi, S., Yoneda, K., and Wu, R. 2005. Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinform. 21, 4205-4208.
51. Xu, J., and Li, Y. 2006. Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22, 2800-2805.