Prediction of protein functions based on function-function correlation relations

Computers in Biology and Medicine

Volumn 40, Issue 3, 2010, Pages 300-305

  Ng, Ka Lok ^a   Ciou, Jin Shuei ^a   Huang, Chien Hung ^b  

^a ASIA UNIVERSITY   (Taiwan)

^b NATIONAL FORMOSA UNIVERSITY   (Taiwan)

Author keywords

Protein function; Protein function function correlation; Protein protein interaction; Yeast protein function prediction

Indexed keywords

BIOLOGICAL INTERPRETATION; CORRELATION RELATIONS; DATA SETS; DEFINED FUNCTIONS; PREDICTION OF PROTEIN FUNCTION; PROTEIN FUNCTIONS; PROTEIN-PROTEIN INTERACTIONS; RANDOMIZATION TESTS; RELATIVE IMPORTANCE; TWO PARAMETER; YEAST PROTEINS;

CORRELATION METHODS; STATISTICAL TESTS; YEAST;

PROTEINS;

FUNGAL PROTEIN;

ARTICLE; CALCULATION; CORRELATION FUNCTION; INTERMETHOD COMPARISON; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN FUNCTION; PROTEIN PROTEIN INTERACTION; SCORING SYSTEM;

PROTEIN BINDING; PROTEINS;

EID: 77649178737     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2010.01.001     Document Type: Article

References (26)

1. Altschul S.F., Madden T.L., Schaffer A.A., Zhang J., Zhang Z., Miller W., and Lipman D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl. Acids Res. 25 (1997) 3389-3402
2. Marcotte E.M., Pellegrini M., Ng H.L., Rice D.W., Yeates T.O., and Eisenberg D. Detecting protein function and protein-protein interactions from genome sequences. Science 285 (1999) 751-753
3. Enright A.J., Iliopoulos I., Kyrpides N.C., and Ouzounis C.A. Protein interaction maps for complete genomes based on gene fusion events. Nature 402 (1999) 86-90
4. Pellegrini M., Marcotte E.M., Thompson M.J., Eisenberg D., and Yeates T.O. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl Acad. Sci. USA 96 (1999) 4285-4288
5. Marcotte E.M., Pellegrini M., Thompson M.J., Yeates T.O., and Eisenberg D. A combined algorithm for genome-wide prediction of protein function. Nature 402 (1999) 83-86
6. P. Pavlidis, J. Weston, Gene functional classification from heterogeneous data, in: Proceedings of the Fifth International Conference on Computational Molecular Biology (RECOMB2001), 2001, 249-255.
7. Zheng Y., Roberts R.J., and Kasif S. Genomic functional annotation using co-evolution profiles of gene clusters. Genome Biol. 3 11 (2002) research0060.1-0060.9
8. Deng M., Zhang K., Mehta S., Chen T., and Sun F. Prediction of protein function using protein-protein interaction data. J. Comput. Biol. 10 (2003) 947-960
9. Vazquez A., Flammini A., Maritan A., and Vespignani A. Global protein function prediction from protein-protein interaction networks. Nat. Biotechnol. 21 (2003) 697-703
10. Bebek G., and Yang J. PathFinder: mining signal transduction pathway segments from protein-protein interaction networks. BMC Bioinformatics 8 (2007) 335
11. Deng M., Chen T., and Sun F. An integrated probabilistic model for functional prediction of proteins. J. Comput. Biol. 11 (2004) 463-475
12. Jiang T., and Keating A.E. AVID: an integrative framework for discovering functional relationships among proteins. BMC Bioinformatics 6 (2005) 136
13. Chua H.N., Sung W.K., and Wong L. An efficient strategy for extensive integration of diverse biological data for protein function prediction. Bioinformatics 23 24 (2007) 3364-3373
14. Kim W.K., Park J., and Suh J.K. Large scale statistical prediction of protein-protein interaction by potentially interacting domain pair. Genome Inf. 13 (2002) 42-50
15. Han D.-S., Kim H.-S., Jang W.-H., Lee S.-D., and Suh J.-K. PreSPI: a domain combination based prediction system for protein-protein interaction. Nucl. Acids Res. 32 (2004) 6312-6320
16. Hirschman J.E., et al. Genome Snapshot: a new resource at the Saccharomyces Genome Database (SGD) presenting an overview of the Saccharomyces cerevisiae genome. Nucl. Acids Res. 34 (2006) D442-D445
17. Xenarios I., Fernandez E., Salwinski L., Duan X., Thompson M., Marcotte E., and Eisenberg D. DIP: the database of interacting proteins. Nucl. Acid Res. 29 (2001) 239-241
18. Salwinski L., Miller C.S., Smith A.J., Pettit F.K., Bowie J.U., and Eisenberg D. The database of interacting proteins. Nucl. Acids Res. 32 (2004) D449-D451
19. Pagel P., Kovac S., Oesterheld M., Brauner B., Dunger-Kaltenbach I., Frishman G., Montrone C., Mark P., Stümpflen V., Mewes H.W., Ruepp A., and Frishman D. The MIPS mammalian protein-protein interaction database. Bioinformatics 21 6 (2005) 832-834
20. Nye Tom M.W., Berzuini C., Gilks W.R., Babu M.M., and Teichmann S.A. Statistical analysis of domains in interacting protein pairs. Bioinformatics 21 (2005) 993-1001
21. Roth V., and Fischer B. Improved functional prediction of proteins by learning kernel combinations in multilable settings. BMC Bioinformatics 8 Suppl 2 (2006) S12
22. Lee H., Tu Z., Deng M., Sun F., and Chen T. Diffusion kernel-based logistic regression models for protein function prediction. OMICS 10 1 (2006) 40-55
23. Gene Ontology Consortium. The gene ontology (GO) project in 2006. Nucl. Acids Res. 34 (2006) D322-D326
24. Ni Q.-S., Zheng-Zhi W., Gang-Guo L., Guang-Yun W., and Ying-Jie Z. Effect of the quality of the interaction data on predicting protein function from protein-protein interactions. Interdiscip. Sci. Comput. Life Sci. 1 1 (2009) 40-45
25. Kohonen J., Talikota S., Corander J., Auvinen P., and Arjas E. A naïve bayes classifier for protein function prediction. In Silico Biol. 9 (2008) 3
26. Nariai N., Kolaczyk E., and Kasif S. Probabilistic protein function prediction form heterogeneous genome-wide data. PLoS ONE 3 (2007) e337