Skip to main content

Ant Colony Optimization for Genome-Wide Genetic Analysis

  • Conference paper
Ant Colony Optimization and Swarm Intelligence (ANTS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5217))

Abstract

In human genetics it is now feasible to measure large numbers of DNA sequence variations across the human genome. Given current knowledge about biological networks and disease processes it seems likely that disease risk can best be modeled by interactions between biological components, which can be examined as interacting DNA sequence variations. The machine learning challenge is to effectively explore interactions in these datasets to identify combinations of variations which are predictive of common human diseases. Ant colony optimization (ACO) is a promising approach to this problem. The goal of this study is to examine the usefulness of ACO for problems in this domain and to develop a prototype of an expert knowledge guided probabilistic search wrapper. We show that an ACO approach is not successful in the absence of expert knowledge but is successful when expert knowledge is supplied through the pheromone updating rule.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Freitas, A.A.: Understanding the crucial role of attribute interaction in data mining. Artif. Intell. Rev. 16(3), 177–199 (2001)

    Article  MATH  Google Scholar 

  2. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell (2002)

    MATH  Google Scholar 

  3. Moore, J.H.: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity 56, 73–82 (2003)

    Article  Google Scholar 

  4. The International HapMap Consortium: A haplotype map of the human genome. Nature 437(7063), 1299–1320 (2005); 10.1038/nature04226

    Google Scholar 

  5. Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Technical report 91-016, Dipartimento di Elettronica e Informatica, Politecnico di Milano (1991)

    Google Scholar 

  6. Parpinelli, R., Lopes, H., Freitas, A.: An Ant Colony Based System for Data Mining: Applications to Medical Data. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 791–797 (2001)

    Google Scholar 

  7. Moore, J.H., White, B.C.: Genome-wide genetic analysis using genetic programming: The critical need for expert knowledge. Genetic Programming Theory and Practice IV (2007)

    Google Scholar 

  8. White, B.C., Gilbert, J.C., Reif, D.M., Moore, J.H.: A statistical comparison of grammatical evolution strategies in the domain of human genetics. In: Proceedings of the IEEE Congress on Evolutionary Computing, pp. 676–682 (2005)

    Google Scholar 

  9. Moore, J.H., White, B.C.: Exploiting expert knowledge in genetic programming for genome-wide genetic analysis. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 969–977. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Greene, C.S., White, B.C., Moore, J.H.: An expert knowledge-guided mutation operator for genome-wide genetic analysis using genetic programming. In: Rajapakse, J.C., Schmidt, B., Volkert, L.G. (eds.) PRIB 2007. LNCS (LNBI), vol. 4774, pp. 30–40. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. Moore, J.H., White, B.C.: Tuning relieff for genome-wide genetic analysis. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds.) EvoBIO 2007. LNCS, vol. 4447, pp. 166–175. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  12. Moore, J.H.: Computational analysis of gene-gene interactions using multifactor dimensionality reduction. Expert Review of Molecular Diagnostics 4(6), 795–803 (2004)

    Article  Google Scholar 

  13. Moore, J.H.: Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. In: Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data. IGI (2007)

    Google Scholar 

  14. Moore, J.H., Gilbert, J.C., Tsai, C.T., Chiang, F.T., Holden, T., 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(2), 252–261 (2006)

    Article  MathSciNet  Google Scholar 

  15. Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., Moore, J.H.: Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. American Journal of Human Genetics 69, 138–147 (2001)

    Article  Google Scholar 

  16. Wilke, R.A., Reif, D.M., Moore, J.H.: Combinatorial pharmacogenetics. Nature Reviews Drug Discovery 4, 911–918 (2005)

    Article  Google Scholar 

  17. Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Machine Learning: Proceedings of the AAA 1992 (1992)

    Google Scholar 

  18. Kononenko, I.: Estimating attributes: Analysis and extension of relief. In: Machine Learning: ECML-1994, vol. 94, pp. 171–182 (1994)

    Google Scholar 

  19. Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of relieff and relieff. Mach. Learn. 53, 23–69 (2003)

    Article  MATH  Google Scholar 

  20. Sokal, R.R., Rohlf, F.J.: Biometry: the principles and practice of statistics in biological research, 3rd edn. W. H. Freeman and Co., New York (1995)

    Google Scholar 

  21. Bullnheimer, B., Hartl, R., Strauss, C.: A new rank-based version of the ant system: a computational study. Central European Journal for Operations Research and Economics 7(1), 25–38 (1999)

    MATH  MathSciNet  Google Scholar 

  22. Stützle, T., Hoos, H.: MAX-MIN Ant System and local search for the traveling salesman problem. IEEE International Conference on Evolutionary Computation 1997, 309–314 (1997)

    Google Scholar 

  23. Stützle, T., Hoos, H.H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)

    Article  Google Scholar 

  24. Merkle, D., Middendorf, M., Schmeck, H.: Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation 6(4), 333–346 (2002)

    Article  Google Scholar 

  25. Gonzalez, G., Uribe, J.C., Tari, L., Brophy, C., Baral, C.: Mining gene-disease relationships from biomedical literature: Weighting protein-protein interactions and connectivity measures. In: Pacific Symposium on Biocomputing, vol. 12, pp. 28–39 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Marco Dorigo Mauro Birattari Christian Blum Maurice Clerc Thomas Stützle Alan F. T. Winfield

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Greene, C.S., White, B.C., Moore, J.H. (2008). Ant Colony Optimization for Genome-Wide Genetic Analysis. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87527-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87526-0

  • Online ISBN: 978-3-540-87527-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics