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Multiagent Systems in Expression Analysis

  • Juan F. De Paz
  • Sara Rodríguez
  • Javier Bajo
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 55)

Abstract

This paper presents a multiagent system for decision support in the diagnosis of leukemia patients. The core of the system is a type of agent that integrates a novel strategy based on a case-based reasoning mechanism to classify leukemia patients. This agent is a variation of the CBP agents and proposes a new model of reasoning agent, where the complex processes are modeled as external services. The agents act as coordinators of Web services that implement the four stages of the case-based reasoning cycle. The multiagent system has been implemented in a real scenario, and the classification strategy includes a novel ESOINN neuronal network and statistics methods to analyze the patient’s data. The results obtained are presented within this paper and demonstrate the effectiveness of the proposed agent model, as well as the appropriateness of using multiagent systems to resolve medical problems in a distributed way.

Keywords

Multiagent Systems Case-Based Reasoning microarray neuronal network  ESOINN Case-based planning 

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References

  1. 1.
    Lander, E., et al.: Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001)CrossRefGoogle Scholar
  2. 2.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  3. 3.
    Glez-Bedia, M., Corchado, J.: A planning strategy based on variational calculus for deliberative agents. Computing and Information Systems Journal 10(1), 2–14 (2002)Google Scholar
  4. 4.
    Furao, S., Ogura, T., Hasegawa: An enhanced self-organizing incremental neural network for online unsupervised learning. Neural Networks 20, 893–903 (2007)zbMATHCrossRefGoogle Scholar
  5. 5.
    Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K., Scherf, U., Speed, T.: Exploration, Normalization, and Summaries of High density Oligonucleotide Array Probe Level Data. Biostatistics 4, 249–264 (2003)zbMATHCrossRefGoogle Scholar
  6. 6.
    Brunelli, R.: Histogram Analysis for Image Retrieval. Pattern Recognition 34, 1625–1637 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Shen, F.: An algorithm for incremental unsupervised learning and topology representation. Ph.D. thesis. Tokyo Institute of Technology, Tokyo (2006)Google Scholar
  8. 8.
    Gariepy, R., Pepe, W.: On the Level sets of a Distance Function in a Minkowski Space. Proceedings of the American Mathematical Society 31(1), 255–259 (1972)Google Scholar
  9. 9.
    Breiman, L., Friedman, J., Olshen, A., Stone, C.: Classification and regression trees. In: Wadsworth International Group, Belmont, California (1984)Google Scholar
  10. 10.
    Quinlan, J.: Discovering rules by induction from large collections of examples. In: Michie, D. (ed.) Expert systems in the micro electronic age, pp. 168–201. Edinburgh University Press, Edinburgh (1979)Google Scholar
  11. 11.
    Chua, A., Ahna, H., Halwanb, B., Kalminc, B., Artifond, E., Barkune, A., Lagoudakisf, M., Kumar, A.: A decision support system to facilitate management of patients with acute gastrointestinal bleeding. Artificial Intelligence in Medicine 42(3), 247–259 (2008)CrossRefGoogle Scholar
  12. 12.
    François, P., Cremilleux, B., Robert, C., Demongeot, J.: MENINGE: A medical consulting system for child’s meningitis. Study on a series of consecutive cases. Artificial Intelligence in Medicine. 32(2), 281–292 (1992)CrossRefGoogle Scholar
  13. 13.
    Quackenbush, J.: Computational analysis of microarray data. Nature Review Genetics 2(6), 418–427 (2001)CrossRefGoogle Scholar
  14. 14.
    Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology Of The Grid: An Open Grid Services Architecture For Distributed Systems Integration. Technical Report of the Global Grid Forum (2002)Google Scholar
  15. 15.
    Leake, D., Kendall-Morwick, J.: Towards case-based support for e-science workflow generation by mining provenance. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 269–283. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Shinawi1, M., Cheung, S.W.: The array CGHnext term and its clinical applications. Drug Discovery Today 13(17-18), 760–770 (2008)CrossRefGoogle Scholar
  17. 17.
  18. 18.
    Wooldridge, M., Jennings, N.: Agent Theories, Architectures, and Languages: a Survey. In: Wooldridge, Jennings (eds.) Intelligent Agents, pp. 1–22. Springer, Berlin (1995)Google Scholar
  19. 19.
    Erl, T.: Service-Oriented Architecture (SOA): Concepts, Technology, and Design. Prentice Hall PTR, Englewood Cliffs (2005)Google Scholar
  20. 20.
    Vittorini, P., Michettia, M., di Orio, F.: A SOA statistical engine for biomedical data. Computer Methods and Programs in Biomedicine 92(1), 144–153 (2008)CrossRefGoogle Scholar
  21. 21.
    Saitou, N., Nie, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4, 406–425 (1987)Google Scholar
  22. 22.
    Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics (1990)Google Scholar
  23. 23.
    Stevens, R., McEntireb, G.C., Greenwooda, M., Zhaoa, J., Wipatc, A., Lic, P.: MyGrid and the drug discovery process. Drug Discovery Today: BIOSILICO 2(4), 140–148 (2004)CrossRefGoogle Scholar
  24. 24.
    Huhns, M., Singh, M.P.: Service-Oriented Computing: Key Concepts and Principles. Internet Computing 9(1), 75–81 (2005)CrossRefGoogle Scholar
  25. 25.
    Arranz, A., Cruz, A., Sanz-Bobi, M.A., Ruíz, P., Coutiño, J.: DADICC: Intelligent system for anomaly detection in a combined cycle gas turbine plant. Expert Systems with Applications 34(4), 2267–2277 (2008)CrossRefGoogle Scholar
  26. 26.
    Contreras, M., Sheremetov, L.: Industrial application integration using the unification approach to agent-enabled semantic SOA. Robotics and Computer-Integrated Manufacturing 24(5), 680–695 (2008)CrossRefGoogle Scholar
  27. 27.
    Tapia, D.I., Rodriguez, S., Bajo, J., Corchado, J.M.: FUSION@, A SOA-Based Multi-agent Architecture. In: International Symposium on Distributed Computing and Artificial Intelligence, Advances in Soft Computing, vol. 50, pp. 99–107 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Juan F. De Paz
    • 1
  • Sara Rodríguez
    • 1
  • Javier Bajo
    • 1
  1. 1.Departamento Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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