ANN based protein function prediction using integrated protein-protein interaction data

Proceedings - 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009

Volumn , Issue , 2009, Pages 271-277

  Shi, Lei ^a   Cho, Young Rae ^a   Zhang, Aidong ^a  

^a the State University of New York   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANN (ARTIFICIAL NEURAL NETWORK); DATA SETS; DISTANCE MATRICES; EXISTING METHOD; FUNCTION PREDICTION; FUNCTIONAL ANNOTATION; GENOMIC ERA; HIGH-THROUGHPUT; PRECISION AND RECALL; PROTEIN FUNCTION PREDICTION; PROTEIN FUNCTIONS; PROTEIN INTERACTION; PROTEIN-PROTEIN INTERACTION NETWORKS; PROTEIN-PROTEIN INTERACTIONS; PROTEOMES; SMALL WORLDS;

BACKPROPAGATION; BIOINFORMATICS; BIOLOGY; COMPUTER SCIENCE; INTELLIGENT COMPUTING; NEURAL NETWORKS; PLASMA WAVES;

PROTEINS;

EID: 70450185457     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCBS.2009.98     Document Type: Conference Paper

References (28)

1. Chua, HN, Sung, W-K and Wong,L : Exploiting indirect neighbors and topological weight to predict protein function from protein protein interactions. Bioinformatics 22(13):1623-1630, 2006. (Pubitemid 43985296)
2. Chen J., Hsu, W., Lee, M. and Ng, S. :Systematic assessment of high-throughput experimental data for reliable protein interactions using network topology. 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'04) pages 368-372, 2004.
3. Deane, M.C. et al. : Protein interactions : two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics, 1:349-356, 2002.
4. Deng, M, Zhang, K, Mehta, S,Chen, T and Sun, F: Prediction of protein function using protein protein interaction data. Journal of Computational biology 10(6):947-960,2003.
5. Gavin A-C, et al. : Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature,415:141-147, 2002
6. Goldberg D.S. and Roth F.P. : Assessing experimentally derived interactions in a small world. Proc. natl. Acad. Sci. USA, 100:4372-4376s, 2003.
7. Hishigaki, H, Nakai, K, Ono, T, Tanigami, A and Takagi, T: Assessment of prediction accuracy of protein function from protein-protein interaction data. Yeast, 18:523-531, 2001.
8. Ho Y, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 415:180-183,2002
9. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y : A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 98:4569-4574, 2001
10. Karaoz,U, Murali, TM, Letovsky,S, Zheng, Y, Ding, C, Cantor, CR and kasif, S: Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl. Acad. Sci. USA 101(9):2888-2893, 2004.
11. Lee, H, Tu, Z, Deng, M, Sun, F and Chen, T: Diffusion Kernel-based logistic regression models for protein function prediction. OMICS A journal of Integrative Biology 10(1): 40-55, 2006.
12. Lockhart, D.J., Dong, H., Byne, M.C.,Follettie, M.T., Gallo,M.V., Chee, M.S., Mittmann, M., Wang, C., Kobayashi, M., Horton, H., Brown, E.L. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol, 14:1675-1680, 1996.
13. Marcotte EM, Pellegrini M, Ng H-L, Rice DW, Yeates TO, Eisenberg D Detecting protein function and protein protein interaction from genome sequences. Science 285: 751:753, 1999.
14. Nabieva, E, Jim, K, Agarwal, A, Chazelle, B and Singh, M: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21:i302-i310, 2005.
15. Mewes, H.W. et al., MIPS : analysis and annotation of proteins from whole genomes in 2005. Nucl Acids Res, 34:D169-D172, 2006.
16. Pearson WR, Lipman DJ. Improved tools for biological sequence comparison. Proc Natl Acad Sci USA 85:2444-2448, 1988.
17. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg d, Yeates TO. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci USA 96:4285:4288, 1999.
18. Pei, P. and Zhang, A. : A toplolgical measurement for weighted protein interaction network. Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference, 268-278,2005
19. Saito, R.,Suzuki, H. and Hayashizaki, Y. :Interaction generality, a measurement to assess the reliability of a protein protein interaction. Nucleic Acids Res, 30:1163-1168, 2002.
20. Saito, R.,Suzuki, H. and Hayashizaki, Y.: Construction of reliable protein-protein interaction networks with a new interaction generality measure. Bioinformatics, 19:756-763,2003.
21. Salwinski, L., et al : The Database of Interacting Proteins: 2004 updata. NAR 32 Database issue, pages D449-51, 2004.
22. Schwikowski, B., Uetz, P. and Fields, S.: A network of protein protein interactions in yeast. Nature Biotechnology,18:1257-1261,2000
23. Sprinzak, E. , Sattath, S. and Margalit, H. How reliable are experimental protein protein interaction data? J. Mol. Biol., 327(5):919-923, Apr.2003
24. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: A General Repository for Interaction Datasets. Nucleic Acids Res. 34:D535-9, 2006 Jan 1
25. Global mapping of the yeast genetic interaction network. Science 303:808-813,2004.
26. Uetz P, et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403:623-627, 2000
27. Vazquez, A, Flammini, A, Maritan, A, and Vespignani, A: Global protein function prediction from protein-protein interaction networks. Nature biotechnology 21(6):697-700, 2003.
28. Watts, D. and Strogatz, S. Collective dynamics of "small-world" networks. Nature, 393(440-442),1998.