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Link to original content: https://doi.org/10.1007/11816102_66
Using a Stochastic AdaBoost Algorithm to Discover Interactome Motif Pairs from Sequences | SpringerLink
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Using a Stochastic AdaBoost Algorithm to Discover Interactome Motif Pairs from Sequences

  • Conference paper
Computational Intelligence and Bioinformatics (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4115))

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Abstract

Protein interactome is an important research focus in the post-genomic era. The identification of interacting motif pairs is essential for exploring the mechanism of protein interactions. We describe a stochastic AdaBoost approach for discovering motif pairs from known interactions and pairs of proteins that are putatively not to interact. Our interacting motif pairs are validated by multiple-chain PDB structures and show more significant than those selected by traditional statistical method. Furthermore, in a cross-validated comparison, our model can be used to predict interactions between proteins with higher sensitivity (66.42%) and specificity (87.38%) comparing with the Naive Bayes model and the dominating model.

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References

  1. Phizicky, E.M., Fields, S.: Protein-Protein Interactions: Methods for Detection and Analysis. Microbiol. Rev. 59(1), 94–123 (1995)

    Google Scholar 

  2. MacBeath, G., Schreiber, S.L.: Printing Proteins as Microarrays for High-Throughput Function Determination. Science 289(5485), 1760–1763 (2000)

    Google Scholar 

  3. Uetz, P., Giot, L., Cagney, G., et al.: A Comprehensive Analysis of Protein-Protein Interactions in Saccharomyces Cerevisiae. Nature 403(6770), 623–627 (2000)

    Article  Google Scholar 

  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), 4569–4574 (2001)

    Article  Google Scholar 

  5. Zhu, H., Bilgin, M., Bangham, R., et al.: Global Analysis of Protein Activities Using Proteome Chips. Science 293(5537), 2101–2105 (2001)

    Article  Google Scholar 

  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), 141–147 (2002)

    Article  Google Scholar 

  7. Ho, Y., Gruhler, A., Heilbut, A., et al.: Systematic Identification of Protein Complexes in Saccharomyces Cerevisiae by Mass Spectrometry. Nature 415(6868), 180–183 (2002)

    Article  Google Scholar 

  8. Mrowka, R., Patzak, A., Herzel, H.: Is There a Bias in Proteome Research? Genome. Res. 11(12), 1971–1973 (2001)

    Article  Google Scholar 

  9. Huynen, M.A., Bork, P.: Measuring Genome Evolution. Proc. Natl. Acad. Sci. U S A 95(11), 5849–5856 (1998)

    Article  Google Scholar 

  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), 4285–4288 (1999)

    Article  Google Scholar 

  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), 86–90 (1999)

    Article  Google Scholar 

  12. Marcotte, E.M., Pellegrini, M., Ng, H.L., et al.: Detecting Protein Function and Protein-Protein Interactions from Genome Sequences. Science 285(5428), 751–753 (1999)

    Article  Google Scholar 

  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), 324–328 (1998)

    Article  Google Scholar 

  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), 2896–2901 (1999)

    Article  Google Scholar 

  15. Wojcik, J., Schachter, V.: Protein-Protein Interaction Map Inference Using Interacting Domain Profile Pairs. Bioinformatics 17(Suppl. 1), S296–S305 (2001)

    Google Scholar 

  16. Deng, M., Mehta, S., Sun, F., et al.: Inferring Domain-Domain Interactions from Protein-Protein Interactions. Genome. Res. 12(10), 1540–1548 (2002)

    Article  Google Scholar 

  17. Kim, W.K., Park, J., Suh, J.K.: Large Scale Statistical Prediction of Protein-Protein Interaction by Potentially Interacting Domain (pid) Pair. In: Genome Inform Ser Workshop Genome Inform, vol. 13, pp. 42–50 (2002)

    Google Scholar 

  18. Bock, J.R., Gough, D.A.: Whole-Proteome Interaction Mining. Bioinformatics 19(1), 125–134 (2003)

    Article  Google Scholar 

  19. Gomez, S.M., Rzhetsky, A.: Towards the Prediction of Complete Protein-Protein Interaction Networks. In: Pac. Symp. Biocomput., pp. 413–424 (2002)

    Google Scholar 

  20. Gomez, S.M., Noble, W.S., Rzhetsky, A.: Learning to Predict Protein-Protein Interactions from Protein Sequences. Bioinformatics 19(15), 1875–1881 (2003)

    Article  Google Scholar 

  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), 6312–6320 (2004)

    Article  Google Scholar 

  22. Hayashida, M., Ueda, N., Akutsu, T.: Inferring Strengths of Protein-Protein Interactions from Experimental Data Using Linear Programming. Bioinformatics 19(Suppl. 2), II58–II65 (2003)

    Google Scholar 

  23. Ng, S.K., Zhang, Z., Tan, S.H.: Integrative Approach for Computationally Inferring Protein Domain Interactions. Bioinformatics 19(8), 923–929 (2003)

    Article  Google Scholar 

  24. Chen, X.W., Liu, M.: Prediction of Protein-Protein Interactions Using Random Decision Forest Framework. Bioinformatics 21(24), 4394–4400 (2005)

    Article  Google Scholar 

  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), 3360–3368 (2005)

    Article  Google Scholar 

  26. Liu, Y., Liu, N., Zhao, H.: Inferring Protein-Protein Interactions through High-Throughput Interaction Data from Diverse Organisms. Bioinformatics 21(15), 3279–3285 (2005)

    Article  Google Scholar 

  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), 993–1001 (2005)

    Article  Google Scholar 

  28. Riley, R., Lee, C., Sabatti, C., Eisenberg, D.: Inferring Protein Domain Interactions from Databases of Interacting Proteins. Genome. Biol. 6(10), R89 (2005)

    Article  Google Scholar 

  29. Lehrach, W.P., Husmeier, D., Williams, C.K.: A Regularized Discriminative Model for the Prediction of Protein-Peptide Interactions. Bioinformatics 22(5), 532–540 (2006)

    Article  Google Scholar 

  30. Sprinzak, E., Margalit, H.: Correlated Sequence-Signatures as Markers of Protein-Protein Interaction. J. Mol. Biol. 311(4), 681–692 (2001)

    Article  Google Scholar 

  31. Wang, H., Segal, E., Ben-Hur, A., et al.: Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale. In: Advances in Neural Information Processing Systems 17, pp. 1465–1472. MIT Press, Cambridge (2005)

    Google Scholar 

  32. Fang, J., Haasl, R.J., Dong, Y., Lushington, G.H.: Discover Protein Sequence Signatures from Protein-Protein Interaction Data. BMC Bioinformatics 6(1), 277 (2005)

    Article  Google Scholar 

  33. Falquet, L., Pagni, M., Bucher, P., et al.: The PROSITE Database, its Status in 2002. Nucleic Acids Res. 30(1), 235–238 (2002)

    Article  Google Scholar 

  34. Yu, H., Qian, M., Deng, M.: Understanding Protein-Protein Interactions: From Domain Level to Motif Level. In: Proceeding of Sino-Germany Conference: Network, From Biology to Theory, Springer, Heidelberg (2005)

    Google Scholar 

  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), 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  36. Salwinski, L., Miller, C.S., Smith, A.J., et al.: The Database of Interacting Proteins: 2004 Update. Nucleic Acids Res. 32(Database issue), D449–D451 (2004)

    Article  Google Scholar 

  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), 535–545 (2004)

    Article  Google Scholar 

  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), D233–D237 (2005)

    Article  Google Scholar 

  39. Taylor, W.R., Jones, D.T.: Deriving an Amino Acid Distance Matrix. J. Theor. Biol. 164(1), 65–83 (1993)

    Article  Google Scholar 

  40. Littlestone, N.: Learning Quickly when Irrelevant Attributes Abound: A New Linear-Threshold Algorithm. Machine Learning 2(4), 285–318 (1988)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LMAM, School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing, 100871, P.R. China

    Huan Yu, Minping Qian & Minghua Deng

Authors
  1. Huan Yu
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  2. Minping Qian
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  3. Minghua Deng
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China

    De-Shuang Huang

  2. Queen’s University, Belfast, UK

    Kang Li  & George William Irwin  & 

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© 2006 Springer-Verlag Berlin Heidelberg

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Yu, H., Qian, M., Deng, M. (2006). Using a Stochastic AdaBoost Algorithm to Discover Interactome Motif Pairs from Sequences. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_66

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  • DOI: https://doi.org/10.1007/11816102_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37277-6

  • Online ISBN: 978-3-540-37282-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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