Automatic Workflow during the Reuse Phase of a CBP System Applied to Microarray Analysis

  • Juan F. De Paz
  • Ana B. Gil
  • Emilio Corchado
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 74)


The application of information technology in the field of biomedicine has become increasingly important over the last several years. The different possibilities for the workflow in the microarray analysis can be huge and it would be very interesting to create an automatic process for establishing the workflows. This paper presents an intelligent dynamic architecture based on intelligent organizations for knowledge data discovery in biomedical databases. The multi-agent architecture incorporates agents that can perform automated planning and find optimal plans. The agents incorporate the CBP-BDI model for developing the automatic planning that makes possible to predict the efficiency of the workflow beforehand These agents propose a new reorganizational agent model in which complex processes are modelled as external services.


Multiagent Systems microarray Case-based planning 


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  1. 1.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  2. 2.
    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
  3. 3.
    Kohavi, R., Ross Quinlan, R.: Decision Tree Discovery Handbook of Data Mining and Knowledge Discovery, pp. 267–276. Oxford University Press, Oxford (2002)Google Scholar
  4. 4.
    Quackenbush, J.: Computational analysis of microarray data. Nature Review Genetics 2(6), 418–427 (2001)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Corchado, J.M., Bajo, J., De Paz, Y., Tapia, D.I.: Intelligent Environment for Monitoring Alzheimer Patients, Agent Technology for Health Care. Decision Support Systems 44(2), 382–396 (2008)CrossRefGoogle Scholar
  7. 7.
    Ardissono, L., Petrone, G., Segnan, M.: A conversational approach to the interaction with Web Services. Computational Intelligence, vol. 20, pp. 693–709. Blackwell Publishing, Malden (2004)Google Scholar
  8. 8.
    Oliva, E., Natali, A., Ricci, A., Viroli, M.: An Adaptation Logic Framework for {J}ava-based Component Systems. Journal of Universal Computer Science 14(13), 2158–2181 (2008)Google Scholar
  9. 9.
    Bratman, M.: Intention, Plans and Practical Reason. Harvard U.P., Cambridge (1987)Google Scholar
  10. 10.
    Corchado, J.M., De Paz, J.F., Rogríguez, S., Bajo, J.: Model of experts for decision support in the diagnosis of leukemia patients. Artificial Intelligence in Medicine 46(3), 179–200 (2009)CrossRefGoogle Scholar
  11. 11.
    Horner, M.J., Ries, L.A.G., Krapcho, M., Neyman, N., Aminou, R., Howlader, N., Altekruse, S.F., Feuer, E.J., Huang, L., Mariotto, A., Miller, B.A., Lewis, D.R., Eisner, M.P., Stinchcomb, D.G., Edwards, B.K. (eds.): SEER Cancer Statistics Review, 1975-2006, National Cancer Institute (2009),
  12. 12.
    Kuo, C.D., Chen, G.Y., Wang, Y.Y., Hung, M.J., Yang, J.L.: Characterization and quantification of the return map of RR intervals by Pearson coefficient in patients with acute myocardial infarction. Autonomic Neuroscience 105(2), 145–152 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Juan F. De Paz
    • 1
  • Ana B. Gil
    • 1
  • Emilio Corchado
    • 1
  1. 1.Departamento Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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