Using a stochastic AdaBoost algorithm to discover interactome motif pairs from sequences

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4115 LNBI -III, Issue , 2006, Pages 622-630

  Yu, Huan ^a   Qian, Minping ^a   Deng, Minghua ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ADA (PROGRAMMING LANGUAGE); MATHEMATICAL MODELS; PROTEINS; RANDOM PROCESSES; SENSITIVITY ANALYSIS; STATISTICAL METHODS;

MULTIPLE-CHAIN PDB STRUCTURES; NAIVE BAYES MODEL; PROTEIN INTERACTOME;

ALGORITHMS;

EID: 33749553748     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11816102_66     Document Type: Conference Paper

References (40)

1. Phizicky, E.M., Fields, S.: Protein-Protein Interactions: Methods for Detection and Analysis. Microbiol Rev 59(1) (1995) 94-123
2. MacBeath, G., Schreiber, S.L.: Printing Proteins as Microarrays for High-Throughput Function Determination. Science 289(5485) (2000) 1760-1763
3. Uetz, P., Giot, L., Cagney, G., et al.: A Comprehensive Analysis of Protein-Protein Interactions in Saccharomyces Cerevisiae. Nature 403(6770) (2000) 623-627
4. Ito, T., Chiba, T., Ozawa, R., et al.: A Comprehensive Two-Hybrid Analysis to Explore the Yeast Protein Interactome. Proc Natl Acad Sci U S A 98(8) (2001) 4569-4574
5. Zhu, H., Bilgin, M., Bangham, R., et al.: Global Analysis of Protein Activities Using Proteome Chips. Science 293(5537) (2001) 2101-2105
6. Gavin, A.C., Bosche, M., Krause, R., et al.: Functional Organization of the Yeast Proteome by Systematic Analysis of Protein Complexes. Nature 415(6868) (2002) 141-147
7. Ho, Y., Gruhler, A., Heilbut, A., et al.: Systematic Identification of Protein Complexes in Saccharomyces Cerevisiae by Mass Spectrometry. Nature 415(6868) (2002) 180-183
8. Mrowka, R., Patzak, A., Herzel, H.: Is There a Bias in Proteome Research? Genome Res 11(12) (2001) 1971-1973
9. Huynen, M.A., Bork, P.: Measuring Genome Evolution. Proc Natl Acad Sci U S A 95(11) (1998) 5849-5856
10. Pellegrini, M., Marcotte, E.M., Thompson, M.J., et al.: Assigning Protein Functions by Comparative Genome Analysis: Protein Phylogenetic Profiles. Proc Natl Acad Sci U S A 96(8) (1999) 4285-4288
11. Enright, A.J., Iliopoulos, I., Kyrpides, N.C., et al.: Protein Interaction Maps for Complete Genomes Based on Gene Fusion Events. Nature 402(6757) (1999) 86-90
12. Marcotte, E.M., Pellegrini, M., Ng, H.L., et al.: Detecting Protein Function and Protein-Protein Interactions from Genome Sequences. Science 285(5428) (1999) 751-753
13. Dandekar, T., Snel, B., Huynen, M., et al.: Conservation of Gene Order: A Fingerprint of Proteins that Physically Interact. Trends Biochem Sci 23(9) (1998) 324-328
14. Overbeek, R., Fonstein, M., D'Souza, M., et al.: The Use of Gene Clusters to Infer Functional Coupling. Proc Natl Acad Sci U S A 96(6) (1999) 2896-2901
15. Wojcik, J., Schachter, V.: Protein-Protein Interaction Map Inference Using Interacting Domain Profile Pairs. Bioinformatics 17 Suppl 1 (2001) S296-S305
16. Deng, M., Mehta, S., Sun, F., et al.: Inferring Domain-Domain Interactions from Protein-Protein Interactions. Genome Res 12(10) (2002) 1540-1548
17. Kim, W.K., Park, J., Suh, J.K.: Large Scale Statistical Prediction of Protein-Protein Interaction by Potentially Interacting Domain (pid) Pair. Genome Inform Ser Workshop Genome Inform 13 (2002) 42-50
18. Bock, J.R., Gough, D.A.: Whole-Proteome Interaction Mining. Bioinformatics 19(1) (2003) 125-134
19. Gomez, S.M., Rzhetsky, A.: Towards the Prediction of Complete Protein-Protein Interaction Networks. Pac Symp Biocomput (2002) 413-424
20. Gomez, S.M., Noble, W.S., Rzhetsky, A.: Learning to Predict Protein-Protein Interactions from Protein Sequences. Bioinformatics 19(15) (2003) 1875-1881
21. Han, D.S., Kim, H.S., Jang, W.H., Lee, S.D., Suh, J.K.: PreSPI: A Domain Combination Based Prediction System for Protein-Protein Interaction. Nucleic Acids Res 32(21) (2004) 6312-6320
22. Hayashida, M., Ueda, N., Akutsu, T.: Inferring Strengths of Protein-Protein Interactions from Experimental Data Using Linear Programming. Bioinformatics 19 Suppl 2 (2003) II58-II65
23. Ng, S.K., Zhang, Z., Tan, S.H.: Integrative Approach for Computationally Inferring Protein Domain Interactions. Bioinformatics 19(8) (2003) 923-929
24. Chen, X.W., Liu, M.: Prediction of Protein-Protein Interactions Using Random Decision Forest Framework. Bioinformatics 21(24) (2005) 4394-4400
25. Espadaler, J., Romero-Isart, O., Jackson, R.M., Oliva, B.: Prediction of Protein-Protein Interactions Using Distant Conservation of Sequence Patterns and Structure Relationships. Bioinformatics 21(16) (2005) 3360-3368
26. Liu, Y., Liu, N., Zhao, H.: Inferring Protein-Protein Interactions through High-Throughput Interaction Data from Diverse Organisms. Bioinformatics 21(15) (2005) 3279-3285
27. Nye, T.M., Berzuini, C., Gilks, W.R., Babu, M.M., Teichmann, S.A.: Statistical Analysis of Domains in Interacting Protein Pairs. Bioinformatics 21(7) (2005) 993-1001
28. Riley, R., Lee, C., Sabatti, C., Eisenberg, D.: Inferring Protein Domain Interactions from Databases of Interacting Proteins. Genome Biol 6(10) (2005) R89
29. Lehrach, W.P., Husmeier, D., Williams, C.K.: A Regularized Discriminative Model for the Prediction of Protein-Peptide Interactions. Bioinformatics 22(5) (2006) 532-540
30. Sprinzak, E., Margalit, H.: Correlated Sequence-Signatures as Markers of Protein-Protein Interaction. J Mol Biol 311(4) (2001) 681-692
31. Wang, H., Segal, E., Ben-Hur, A., et al.: Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale. Advances in Neural Information Processing Systems 17, MIT Press (2005) 1465-1472
32. Fang, J., Haasl, R.J., Dong, Y., Lushington, G.H.: Discover Protein Sequence Signatures from Protein-Protein Interaction Data. BMC Bioinformatics 6(1) (2005) 277
33. Falquet, L., Pagni, M., Bucher, P., et al.: The PROSITE Database, its Status in 2002. Nucleic Acids Res 30(1) (2002) 235-238
34. Yu, H., Qian, M., Deng, M.: Understanding Protein-Protein Interactions: From Domain Level to Motif Level. Proceeding of Sino-Germany Conference: Network, From Biology to Theory, Springer-Verlag (2005) in press
35. Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55(1) (1997) 119-139
36. Salwinski, L., Miller, C.S., Smith, A.J., et al.: The Database of Interacting Proteins: 2004 Update. Nucleic Acids Res 32(Database issue) (2004) D449-D451
37. Jansen, R., Gerstein, M.: Analyzing Protein Function on a Genomic Scale: The Importance of Gold-Standard Positives and Negatives for Network Prediction. Curr Opin Microbiol 7(5) (2004) 535-545
38. Deshpande, N., Addess, K.J., Bluhm, W.F., et al.: The RCSB Protein Data Bank: A Redesigned Query System and Relational Database Based on the mmCIF Schema. Nucleic Acids Res 33(Database issue) (2005) D233-D237
39. Taylor, W.R., Jones, D.T.: Deriving an Amino Acid Distance Matrix. J Theor Biol 164(1) (1993) 65-83
40. Littlestone, N.: Learning Quickly when Irrelevant Attributes Abound: A New Linear-Threshold Algorithm. Machine Learning 2(4) (1988) 285-318