Transitive closure and metric inequality of weighted graphs: detecting protein interaction modules using cliques

International Journal of Data Mining and Bioinformatics

Volumn 1, Issue 2, 2006, Pages 162-177

  Ding, Chris ^a   He, Xiaofeng ^a   Xiong, Hui ^a   Peng, Hanchuan ^a   Holbrook, Stephen R ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

protein interaction; transitive closure; triangle inequality; ultrametric; weighted graph

Indexed keywords

FUNGAL PROTEIN;

ALGORITHM; ARTICLE; BIOLOGY; METABOLISM; METHODOLOGY; PROTEIN ANALYSIS; PROTEIN BINDING; SYSTEMS BIOLOGY; THEORETICAL MODEL;

ALGORITHMS; COMPUTATIONAL BIOLOGY; FUNGAL PROTEINS; MODELS, THEORETICAL; PROTEIN BINDING; PROTEIN INTERACTION MAPPING; SYSTEMS BIOLOGY;

EID: 43549087161     PISSN: 17485673     EISSN: 17485681     Source Type: Journal    
DOI: 10.1504/IJDMB.2006.010854     Document Type: Article

References (14)

1. Bader, G.D. and Hogue, C.W.V. (2002) ‘Analyzing yeast protein-protein interaction data obtained from different sources’, Nature Biotechnology, Vol. 20, pp.991–997.
2. Ding, C., He, X., Meraz, R. and Holbrook, S. (2004) ‘A unified representation for multi-protein complex data for modeling protein interaction networks’, Proteins: Structure, Function, and Bioinformatics, Vol. 57, pp.99–108.
3. 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., Gnau, 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. (2002) ‘Functional organization of the yeast proteome by systematic analysis of protein complexes’, Nature, Vol. 415, pp.141–147.
4. Hastie, T., Tibshirani, R. and Friedman, J. (2001) Elements of Statistical Learning, Springer Verlag, New York, Berlin, Heidelberg.
5. Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., Adams, S.L., 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., Figeys, D. and Tyers, M. (2002) ‘Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry’, Nature, Vol. 415, pp.180–193.
6. Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M. and Sakaki, Y. (2001) ‘A comprehensive two hybrid analysis to explore the yeast protein interactome’, Proc. Natl. Acad. Sci., Vol. 98, No. 8, pp.4569–4574.
7. Motzkin, T. and Straus, E. (1965) ‘Maxima for graphs and a new proof of a theorem of turan’, Canad. J. Math., Vol. 17, pp.533–540.
8. Pelillo, M., Siddiqi, K. and Zucker, S. (1999) ‘Matching hierarchical structures using association graphs’, IEEE. Trans. on Pattern Analysis and Machine Intelligence, Vol. 21, pp.1105—1120.
9. Tibshirani, R. (1996) ‘Regression shrinkage and selection via the LASSO’, J. Royal. Statist. SocB., Vol. 58, pp.267–288.
10. Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., Qureshi-Emili, A., Li, Y., Godwin, B., Conover, D., Kalbfleisch, T., Vijayadamodar, G., Yang, M., Johnston, M., Fields, S. and Rothberg, J.M. (2000) ‘A comprehensive analysis of protein-protein interactions in saccharomyces cerevisiae’, Nature, Vol. 403, No. 6770, pp.623–627.
11. Wasserman, S. and Faust, K. (1994) Social Network Analysis, Cambridge University Press, Cambridge.
12. Watts, D. and Strogatz, S. (1998) ‘Collective dynamics of small world networks’, Nature, Vol. 393, pp.440–442.
13. Xiong, H., He, X., Ding, C., Zhang, Y., Kumar, V. and Holbrook, S. (2005) ‘Identification of functional modules in protein complexes via hyperclique pattern discovery’, Proc. Pacific Symposium on Biocomputing, pp.221–232.
14. Using L2 constraint, the solution is the principal eigenvector of A. By Perron’s Theorem, all entries of the principal eigenvector are nonnegative.