Advertisement

Ensemble Based Data Fusion for Gene Function Prediction

  • Matteo Re
  • Giorgio Valentini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

The availability of an ever increasing amount of data sources due to recent advances in high throughput biotechnologies opens unprecedented opportunities for genome-wide gene function prediction. Several approaches to integrate heterogeneous sources of biomolecular data have been proposed in literature, but they suffer of drawbacks and limitations that we could in principle overcome by applying multiple classifier systems. In this work we evaluated the performances of three basic ensemble methods to integrate six different sources of high-dimensional biomolecular data. We also studied the performances resulting from the application of a simple greedy classifier selection scheme, and we finally repeated the entire experiment by introducing a feature filtering step. The experimental results show that data fusion realized by means of ensemble-based systems is a valuable research line for gene function prediction.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pena-Castillo, L., et al.: A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biology 9 (2008)Google Scholar
  2. 2.
    Noble, W., Ben-Hur, A.: Integating information for protein function prediction. In: Bioinformatics - From Genomes to Therapies, pp. 1297–1314. Wiley, Chichester (2007)CrossRefGoogle Scholar
  3. 3.
    des Jardins, M., et al.: Prediction of enzyme classification from protein sequence without the use of sequence similarity. In: Proc. of the 5th ISMB, pp. 92–99 (1997)Google Scholar
  4. 4.
    Karaoz, U., et al.: Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl. Acad. Sci. USA 101, 2888–2893 (2004)CrossRefGoogle Scholar
  5. 5.
    Lanckriet, G., De Bie, T., Cristianini, N., Jordan, M., Noble, W.: A statistical framework for genomic data fusion. Bioinformatics 20, 2626–2635 (2004)CrossRefGoogle Scholar
  6. 6.
    Pavlidis, P., Weston, J., Cai, J., Noble, W.: Learning gene functional classification from multiple data. J. Comput. Biol. 9, 401–411 (2002)CrossRefGoogle Scholar
  7. 7.
    Guan, Y., et al.: Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biology 9 (2008)Google Scholar
  8. 8.
    Polikar, R., et al.: An ensemble based data fusion approach for early diagnosis of Alzheimer disease. Information Fusion 9, 83–95 (2008)CrossRefGoogle Scholar
  9. 9.
    Benediktsson, J., Chanussot, J., Fauvel, M.: Multiple classifier systems in remote sensing: From basics to recent developments. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 501–512. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Lin, H., Lin, C., Weng, R.: A note on Platt’s probabilistic outputs for support vector machines. Machine Learning 68, 267–276 (2007)CrossRefGoogle Scholar
  11. 11.
    Kuncheva, L., Bezdek, J., Duin, R.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition 34, 299–314 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B 57, 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Roli, F., Giacinto, G., Vernazza, G.: Methods for Designing Multiple Classifier Systems. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 78–87. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Partridge, D., Yates, W.: Engineering multiversion neural-net systems. Neural Computation 8, 869–893 (1996)CrossRefGoogle Scholar
  15. 15.
    Stark, C., et al.: BioGRID: a general repository for interaction datasets. Nucl. Acids Res. 34, D535–D539 (2006)CrossRefGoogle Scholar
  16. 16.
    von Mering, C., et al.: STRING: a database of predicted functional associations between proteins. Nucl. Acids Res. 31, 258–261 (2003)CrossRefGoogle Scholar
  17. 17.
    Finn, R., et al.: The Pfam protein families database. Nucl. Acids Res. 36, 281–288 (2008)CrossRefGoogle Scholar
  18. 18.
    Gasch, P., et al.: Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241–4257 (2000)CrossRefGoogle Scholar
  19. 19.
    Spellman, P., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomices cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998)CrossRefGoogle Scholar
  20. 20.
    Ruepp, A., et al.: The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucl. Acids Res. 32, 5539–5545 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Matteo Re
    • 1
  • Giorgio Valentini
    • 1
  1. 1.DSI, Dipartimento di Scienze dell’ InformazioneUniversità degli Studi di MilanoMilanoItalia

Personalised recommendations