Predicting protein-protein interaction based on fisher scores extracted from domain profiles

Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Volumn , Issue , 2007, Pages 946-951

  Patel, Tapan ^a   Liao, Li ^a  

^a University of Delaware   (United States)

Author keywords

Feacture seleletion; Fisher scores; Profile hidden Markov models; Protein protein interaction; Support vector machines

Indexed keywords

BIOINFORMATICS; COMPUTATIONAL GRAMMARS; FLOW INTERACTIONS; GEARS; IMAGE RETRIEVAL; LEARNING SYSTEMS; MARKOV PROCESSES; MULTILAYER NEURAL NETWORKS; OBJECT RECOGNITION; SUPPORT VECTOR MACHINES; VECTORS; WAVELET TRANSFORMS;

CROSS VALIDATIONS; DO-MAINS; DOMAIN LEVELS; FEACTURE SELELETION; FISHER SCORES; GENERAL APPROACHES; INTERACTION SITES; MACHINE LEARNING METHODS; PREDICTION ACCURACIES; PROFILE HIDDEN MARKOV MODELS; PROTEIN DOMAINS; PROTEIN SEQUENCES;

HIDDEN MARKOV MODELS;

EID: 47649108389     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2007.4375672     Document Type: Conference Paper

References (22)

1. Ito, T. et al. 2001. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA, 98: 4569-4574.
2. Ueta, P. et al 2000. A comprehensive analysis of protein-protein interactions in Saccharimyces cerevisiae. Nature, 403: 623-627.
3. Gomez, SM, Noble, WS and Rzhetsky, A. Learning to predict protein-protein interactions from protein sequences. Bioinformatics, 19:1875-1881, 2003.
4. Bock JR, Gough DA: Predicting protein-protein interactions from primary structure. Bioinformatics 2001, 17:455-460.
5. Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, Eisenberg D: Detecting protein function and protein-protein interactions from genome sequences. Science 1999, 285:751-753.
6. Pazos F, Valencia A: Similarity of phylogenetic trees as indicator of protein-protein interaction. Protein Eng 2001, 14:609-614.
7. Goh CS, Bogan AA, Joachimiak M, Walther D, Cohen FE: Co-evolution of proteins with their interaction partners. J Mol Biol 2000, 299:283-293.
8. Goh C, Cohen FE: Co-evolutionary analysis reveals insights into protein-protein interactions. J Mol Biol 2002, 324:177-192.
9. Deng M, Mehta S, Sun F, Chen T: Inferring domain-domain interactions from protein-protein interactions. Genome Res 2002, 12:1540-1548.
10. Ng SK, Zhang Z, Tan SH: Integrative approach for computationally inferring protein domain interactions. Bioinformatics2003, 19:923-929.
11. Liu Y, Liu N, Zhao H: Inferring protein-protein interactions through high-throughput interaction data from diverse organisms. Bioinformatics 2005, 21:3279-3285.
12. Patel, T. Pillay, M., Jawa R. and Liao L. 2006. Information of binding sites improves protein-protein prediction. The proceedings of the International Conference on Machine Learning and Applications (ICMLA), pp. 205-210, Orlando, Florida.
13. Craig, R.A. and Liao, L. 2007. Phylogenetic Tree Information Aids Supervised Learning for Predicting Protein-Protein Interaction Based on Distance Matrices. BMC Bioinformatics 2007, 8:6.
14. Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller. L., Eddy, S.R., Griffiths-Jones S., Howe, K.L., Marshall, M., and Sonnhammer, E.L. The Pfam Protein Families Database. Nucleic Acids Res. 30: 276-280. 2002.
15. Z. Itzhaki, E. Akiva, Y. Altuvia and H. Margalit. Evolutionary conservation of domain-domain interactions. Genome Biology 7:R125, 2006.
16. Friedrich, T., Pils, B., Dandekar, T., Schultz, J., and Muller T. (2006) Modeling Interaction Sites in Protein Domains with Interaction Profile Hidden Markov Models. Bioinformatics, 22, 2851-2857.
17. T. Jaakkola, M. Diekhans, D. Haussler "A discriminative framework for detecting remote protein homologies" Computational Biology, 7, pp. 95-114.
18. Vapnik V. The Nature of Statistical Learning Theory, Springer Verlag, 1995
19. Cristianini N and Shawe-Taylor, J, Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, UK, 2000.
20. Durbin, R. Eddy, S. Krogh, A. and Mitchison, G. Biological Sequence Analysis. Cambridge University Press, 1998.
21. Joachims T. Marking large-scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Scholkopf and C. Burges and A. Smola (e.d), MIT Press, 1999.
22. Berman, H.M. et al. 2000. The protein data bank. Nucleic Acids Res. 28: 235-242.