Inferring protein-protein interactions by combinatorial models

IFMBE Proceedings

Volumn 14, Issue 1, 2007, Pages 183-186

  Zhang, Xiang Sun ^a   Wang, Rui Sheng ^b   Wu, Ling Yun ^a   Zhang, Shi Hua ^a   Chen, Luonan ^c,d  

^a CHINESE ACADEMY OF SCIENCES   (China)

^b RENMIN UNIVERSITY OF CHINA   (China)

^c SHANGHAI UNIVERSITY   (China)

^d OSAKA SANGYO UNIVERSITY   (Japan)

Author keywords

Domain domain interaction; Integer linear programming; Parsimony principle; Protein protein interaction

Indexed keywords

BIOMEDICAL ENGINEERING; INTEGER PROGRAMMING; MAXIMUM LIKELIHOOD; SIGNAL TRANSDUCTION;

DETERMINISTIC MODELING; DOMAIN-DOMAIN INTERACTIONS; INTEGER LINEAR PROGRAMMING; LINEAR PROGRAMMING RELAXATION; MAXIMUM LIKELIHOOD METHODS; PARSIMONY PRINCIPLE; PHYSICAL INTERACTIONS; PROTEIN-PROTEIN INTERACTIONS;

PROTEINS;

EID: 80055069327     PISSN: 16800737     EISSN: 14339277     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-36841-0_54     Document Type: Conference Paper

References (16)

1. Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., and Sakaki, Y. A comprehensive two hybrid analysis to explore the yeastprotein interactome. Proc. Natl. Acad. Sci., 2001, 98(8):4569-4574.
2. Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R., et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 2000, 403:623-627.
3. Ho, Y., Gruhler, A., and Heilbut, A. et al. Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry. Nature, 2002, 415:180-183.
4. Gavin, A.C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature, 2002, 415, 141-147.
5. Gomez, S.M. et al. Learning to predict protein-protein interaction from protein sequences. Bioinformatics, 2003, 19, 1875-1881.
6. Ramani, A.K. and Marcotte, E.M. Exploiting the co-evolution of interacting proteins to discover interaction specificity. Jouranl of Molecular Biology, 2003, 327, 273-284.
7. Enright, A.J. et al. Protein interaction maps for complete genomes based on gene fusion events. Nature, 1999, 402, 86-90.
8. Deng, M., Mehta, S., Sun, F., and Chen, T. Inferring domain-domain interactions from protein-protein interactions. Genome Research, 2002, 12:1540-1548.
9. Liu, Y., Liu, N., and Zhao, H. Inferring protein-protein interactions through high-throughput interaction data from diverse organisms. Bioinformatics, 2005, 21(15): 3279-3285.
10. Riley, R., Lee, C., Sabatti, C., and Eisenberg, D. Inferring protein domain interactions from databases of interacting proteins. Genome Biology, 2005, 6:R89.
11. Hayashida, M., Ueda, N. and Akutsu, T. Inferring strengths of protein-protein interactions from experimental data using linear programming. Bioinformatics, 2003, 19, ii58–ii65.
12. Chen, L., Wu, L., Wang, Y., Zhang, X. Inferring protein interactions from experimental data by association probabilistic method. Proteins: Structure, Function and Bioinformatics, DOI: 10.1002/prot.20783, 2006.
13. Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O'Shea EK: Global analysis of protein localization in budding yeast. Nature (2003), 425(6959):686-691.
14. Garey, M.R., Johson, D.S. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Co., San Franciscon CA, 1979.
15. Jansen R, Gerstein M: Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Current Opinion in Microbiology (2004), 7(5):535-545.
16. H. W. Mewes et al. Mips: A database for genomesand protein sequences. Nucleic Acids Res., 30(1):31.34, 2000.