Advertisement

Grid-Enabled Framework for Large-Scale Analysis of Gene-Gene Interactions

  • Moez Ben Haj Hmida
  • Yahya Slimani
Part of the Communications in Computer and Information Science book series (CCIS, volume 162)

Abstract

One of the biggest challenges for nowadays biologist is the identification, characterization and gene-gene interactions detection for common human diseases such as cancer and diabetes. This challenge is partly due to the explosion of biological information. The multifactor dimensionality reduction (MDR) method can be used to address this problem. This method can be computationally intensive, especially when more than ten polymorphisms need to be evaluated. The Grid is a promising architecture for genomics problems providing high computing capabilities. In this paper, we describe a framework for supporting the MDR method on Grid environments. This framework helps biologists to automate the execution of multiple tests of gene-gene interactions detection.

Keywords

Genomics gene-gene interaction data mining grid computing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Briollais, L., Wang, Y., Rajendram, I., Onay, V., Shi, E., Knight, J., Ozcelik, H.: Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario. BMC Med 5:22 (2007)Google Scholar
  2. 2.
    Bush, W.S., Dudek, S.M., Ritchie, M.D.: Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions. Bioinformatics 17, 2173–2174 (2006)CrossRefGoogle Scholar
  3. 3.
    Christensen, E., Curbera, F., Meredith, G., Weerawarana, S.: Web Services Description Language (WSDL) 1.1, W3C Note 15 March (2001), http://www.w3.org/TR/2001/NOTE-wsdl-20010315
  4. 4.
    Computational Genetics Laboratory, http://www.epistasis.org/software.html
  5. 5.
    Czajkowski, K.: The WS-Resource Framework Version 1.0 (2004), http://www-106.ibm.com/developerworks/library/ws-resource/ws-wsrf.pdf
  6. 6.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  7. 7.
  8. 8.
    Halling-Brown, M.D., Moss, D.S., Sansom, C.E., Shepherd, A.J.: A computational Grid framework for immunological applications. Phil. Trans. R. Soc. A. 367, 2705–2716 (2009)CrossRefGoogle Scholar
  9. 9.
    Khoussainov, R., Zuo, X., Kushmerick, N.: Grid-enabledweka: A toolkit for machine learning on the grid. ERCIM News (59) (2004)Google Scholar
  10. 10.
    Moore, J.H., Gilbert, J.C., Tsai, C.-T., Chiang, F.T., Holden, W., Barney, N., White, B.C.: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Journal of Theoretical Biology 241, 252–261 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Pena, J.M., Robles, V., Herrero, P., Snchez, A., Prez, M.S.: Adapting the weka data mining toolkit to a grid based environment. In: 3rd Atlantic Web Intelligence Conference, Lodz, pp. 492–497 (2005)Google Scholar
  12. 12.
    Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., Moore, J.H.: Multifactor-dimensionality reduction reveals highorder interactions among estrogen metabolism genes in sporadic breast cancer. American Journal of Human Genetics 69, 138–147 (2001)CrossRefGoogle Scholar
  13. 13.
    Talia, D., Trunfio, P., Verta, O.: Weka4WS: A WSRF-Enabled Weka Toolkit for Distributed Data Mining on Grids. In: the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, pp. 309–320 (2005)Google Scholar
  14. 14.
    Tuecke, S., Czajkowski, K., Foster, I., Frey, J., Graham, S., Kesselman, C., Maguire, T., Sandholm, T., Vanderbilt, P., Snelling, D.: Open Grid Services Infrastructure (OGSI) Version 1.0, Global Grid Forum Draft Recommendation (2002)Google Scholar
  15. 15.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Moez Ben Haj Hmida
    • 1
  • Yahya Slimani
    • 1
  1. 1.Computer Science DepartmentFaculty of Sciences of TunisEl ManarTunisia

Personalised recommendations