Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Data Integration in the Life Sciences

DILS 2008: Data Integration in the Life Sciences pp 177–191Cite as

Combining One-Class Classification Models Based on Diverse Biological Data for Prediction of Protein-Protein Interactions

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5109))

Abstract

This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse biological data. Gold Standard data sets frequently employed for this task contain a high proportion of instances related to ribosomal proteins. We demonstrate that this situation biases the classification results and additionally that the prediction of non-ribosomal based PPI is a much more difficult task. In order to improve the performance of this subtask we have integrated more biological data into the classification process, including data from mRNA expression experiments and protein secondary structure information. Furthermore we have investigated several strategies for combining diverse one-class classification (OCC) models generated from different subsets of biological data. The weighted average combination approach exhibits the best results, significantly improving the performance attained by any single classification model evaluated.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., Qureshi-Emili, A., Li, Y., Godwin, B., Conover, D., Kalbfleisch, T., Vijayadamodar, G., Yang, M., Johnston, M., Fields, S., Rothberg, J.M.: A comprehensive analysis of protein-protein interactions in saccharomyces cerevisiae. Nature 403, 623–627 (2000)

    Article  Google Scholar 

  2. 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. 98, 4569–4574 (2001)

    Article  Google Scholar 

  3. Gavin, A.C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J.M., Michon, A.M., Cruciat, C.M., Remor, M., Hofert, C., Schelder, M., Brajenovic, M., Ruffner, H., Merino, A., Klein, K., Hudak, M., Dickson, D., Rudi, T., Gnau, V., Bauch, A., Bastuck, S., Huhse, B., Leutwein, C., Heurtier, M.A., Copley, R.R., Edelmann, A., Querfurth, E., Rybin, V., Drewes, G., Raida, M., Bouwmeester, T., Bork, P., Seraphin, B., Kuster, B., Neubauer, G., Superti-Furga, G.: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002)

    Article  Google Scholar 

  4. Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., Adams, S.L., Millar, A., Taylor, P., Bennett, K., Boutilier, K., Yang, L., Wolting, C., Donaldson, I., Schandorff, S., Shewnarane, J., Vo, M., Taggart, J., Goudreault, M., Muskat, B., Alfarano, C., Dewar, D., Lin, Z., Michalickova, K., Willems, A.R., Sassi, H., Nielsen, P.A., Rasmussen, K.J., Andersen, J.R., Johansen, L.E., Hansen, L.H., Jespersen, H., Podtelejnikov, A., Nielsen, E., Crawford, J., Poulsen, V., Srensen, B.D., Matthiesen, J., Hendrickson, R.C., Gleeson, F., Pawson, T., Moran, M.F., Durocher, D., Mann, M., Hogue, C.W.V., Figeys, D., Tyers, M.: Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002)

    Article  Google Scholar 

  5. von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S., Bork, P.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403 (2002)

    Article  Google Scholar 

  6. Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F., Gerstein, M.: A bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453 (2003)

    Article  Google Scholar 

  7. Lin, N., Wu, B., Jansen, R., Gerstein, M., Zhao, H.: Information assessment on predicting protein-protein interactions. BMC Bioinformatics 5(154) (2004)

    Google Scholar 

  8. Zhang, L., Wong, S., King, O., Roth, F.: Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 5(38) (2004)

    Google Scholar 

  9. Lu, L.J., Xia, Y., Paccanaro, A., Yu, H., Gerstein, M.: Assessing the limits of genomic data integration for predicting protein networks. Genome Res. 15, 945–953 (2005)

    Article  Google Scholar 

  10. Ben-Hur, A., Noble, W.S.: Kernel methods for predicting protein-protein interactions. Bioinformatics 21(suppl. 1), i38–i46 (2005)

    Article  Google Scholar 

  11. Qi, Y., Bar-Joseph, Z., Klein-Seetharaman, J.: Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins: Structure, Function, and Bioinformatics 63, 490–500 (2006)

    Article  Google Scholar 

  12. Ben-Hur, A., Noble, W.S.: Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinformatics 7(S2) (2006)

    Google Scholar 

  13. Reyes, J.A., Gilbert, D.: Prediction of protein-protein interactions using one-class classification methods and integrating diverse data. Journal of Integrative Bioinformatics 4 (2007)

    Google Scholar 

  14. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54, 45–66 (2004)

    Article  MATH  Google Scholar 

  15. Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations 6, 1–6 (2004)

    Article  Google Scholar 

  16. Mewes, H.W., Frishman, D., Guldener, U., Mannhaupt, G., Mayer, K., Mokrejs, M., Morgenstern, B., Munsterkotter, M., Rudd, S., Weil, B.: Mips: a database for genomes and protein sequences. Nucl. Acids Res. 30, 31–34 (2002)

    Article  Google Scholar 

  17. Browne, F., Wang, H., Zheng, H., Azuaje, F.: An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions. Journal of Integrative Bioinformatics 3 (2006)

    Google Scholar 

  18. Hughes, T.R., Marton, M.J., Jones, A.R., Roberts, C.J., Stoughton, R., Armour, C.D., Bennett, H.A., Coffey, E., Dai, H., He, Y.D., Kidd, M.J., King, A.M., Meyer, M.R., Slade, D., Lum, P.Y., Stepaniants, S.B., Shoemaker, D.D., Gachotte, D., Chakraburtty, K., Simon, J., Bard, M., Friend, S.H.: Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000)

    Article  Google Scholar 

  19. Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T.G., Gabrielian, A.E., Landsman, D., Lockhart, D.J., Davis, R.W.: A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2, 65–73 (1998)

    Article  Google Scholar 

  20. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000)

    Google Scholar 

  21. Drummond, C., Holte, R.C.: Learning to live with false alarms. In: Workshop on Data Mining Methods for Anomaly Detection, Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2005)

    Google Scholar 

  22. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

  23. Joachims, T.: Making large-scale support vector machine learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in kernel methods: support vector learning, pp. 169–184. MIT Press, Cambridge (1999)

    Google Scholar 

  24. Van Berlo, R.J.P., Wessels, L.F., Ridder, D.D.E., Reinders, M.J.T.: Protein complex prediction using an integrative bioinformatics approach. J. Bioinform. Comput. Biol. 5, 839–864 (2007)

    Article  Google Scholar 

  25. Tax, D.M.J.: Ddtools, the Data Description Toolbox for Matlab, http://www-ict.ewi.tudelft.nl/~davidt/dd_tools.html

  26. Guo, Z., Li, Y., Gong, X., Yao, C., Ma, W., Wang, D., Li, Y., Zhu, J., Zhang, M., Yang, D., Wang, J.: Edge-based scoring and searching method for identifying condition-responsive protein protein interaction sub-network. Bioinformatics 23, 2121–2128 (2007)

    Article  Google Scholar 

  27. Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., Botstein, D., Brown, P.O.: Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241–4257 (2000)

    Google Scholar 

  28. Neuvirth, H., Raz, R., Schreiber, G.: Promate: a structure based prediction program to identify the location of protein-protein binding sites. J. Mol. Biol. 338, 181–199 (2004)

    Article  Google Scholar 

  29. Hoskins, J., Lovell, S., Blundell, T.L.: An algorithm for predicting protein-protein interaction sites: Abnormally exposed amino acid residues and secondary structure elements. Protein Sci. 15, 1017–1029 (2006)

    Article  Google Scholar 

  30. Guharoy, M., Chakrabarti, P.: Secondary structure based analysis and classification of biological interfaces: identification of binding motifs in protein protein interactions. Bioinformatics 23, 1909–1918 (2007)

    Article  Google Scholar 

  31. Zhou, H.X., Qin, S.: Interaction-site prediction for protein complexes: a critical assessment. Bioinformatics 23, 2203–2209 (2007)

    Article  Google Scholar 

  32. Cheng, J., Randall, A.Z., Sweredoski, M.J., Baldi, P.: SCRATCH: a protein structure and structural feature prediction server. Nucl. Acids Res. 33(suppl-2), W72–W76 (2005)

    Article  Google Scholar 

  33. Fontana, P., Bindewald, E., Toppo, S., Velasco, R., Valle, G., Tosatto, S.C.E.: The SSEA server for protein secondary structure alignment. Bioinformatics 21, 393–395 (2005)

    Article  Google Scholar 

  34. Cheng, J., Baldi, P.: A machine learning information retrieval approach to protein fold recognition. Bioinformatics 22, 1456–1463 (2006)

    Article  Google Scholar 

  35. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  36. Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51, 181–207 (2003)

    Article  MATH  Google Scholar 

  37. Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Diversity in search strategies for ensemble feature selection. Information Fusion 6, 83–98 (2005)

    Article  Google Scholar 

  38. Tang, E.K., Suganthan, P.N., Yao, X.: An analysis of diversity measures. Machine Learning 65, 247–271 (2006)

    Article  Google Scholar 

  39. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 832–844 (1998)

    Article  Google Scholar 

  40. Yule, G.U.: On the association of attributes in statistics. Philosophical Transactions of the Royal Society of London A(194), 257–319 (1900)

    Article  Google Scholar 

  41. Kohavi, R., Wolpert, D.: Bias plus variance decomposition for zero-one loss functions. In: 13th International Conference on Machine Learning, pp. 275–283. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

  42. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Chichester (2004)

    Google Scholar 

  43. Duin, R.: The combining classifier: to train or not to train? In: 16th International Conference on Pattern Recognition, vol. 2, pp. 765–770 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK, G12 8QQ

    José A. Reyes & David Gilbert

  2. Facultad de Ingeniería, Universidad de Talca, Chile

    José A. Reyes

Authors
  1. José A. Reyes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Gilbert
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Amos Bairoch Sarah Cohen-Boulakia Christine Froidevaux

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Reyes, J.A., Gilbert, D. (2008). Combining One-Class Classification Models Based on Diverse Biological Data for Prediction of Protein-Protein Interactions. In: Bairoch, A., Cohen-Boulakia, S., Froidevaux, C. (eds) Data Integration in the Life Sciences. DILS 2008. Lecture Notes in Computer Science(), vol 5109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69828-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69828-9_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69827-2

  • Online ISBN: 978-3-540-69828-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation