Advertisement

Supervised Neuro-fuzzy Clustering for Life Science Applications

  • Jürgen Paetz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4345)

Abstract

Classification, clustering and rule generation are important tasks in multidimensional data analysis. The combination of clustering or classification with rule generation gives an explanation for the achieved results. Especially in life science applications experts are interested in explanations to understand the underlying data. The usage of supervised neuro-fuzzy systems is a suitable approach for this combined task. Not always classification labels are available for the data when considering new problem areas in life science. Since we had already used a supervised neuro-fuzzy system for some applications, our aim in the case studies was to use the same neuro-fuzzy classifier for clustering, generating understandable rules also for clusters. To do so, we added Monte-Carlo random data to the original data and performed the clustering task with the present classifier in the medical, chemical, and biological domain.

Keywords

Class Label Virtual Screening Test Frequency Septic Shock Patient Cluster Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hardaway, R.M.: A review of septic shock. American Surgeon 66, 22–29 (2000)Google Scholar
  2. 2.
    Schneider, G., Wrede, P.: Artificial neural networks for computer-based molecular design. Biophysics & Molecular Biology 70, 175–222 (1998)CrossRefGoogle Scholar
  3. 3.
    Brause, R.: Adaptive modeling of biochemical pathways. In: Proc. of the 15th IEEE Int. Conf. on Tools with Artificial Intelligence, Sacramento, CA, USA, pp. 62–68 (2003)Google Scholar
  4. 4.
    Gardner, M.J.: The genome of the malaria parasite. Current Opinion in Genetics and Development 9, 704–708 (1999)CrossRefGoogle Scholar
  5. 5.
    Duda, R.O., Stork, D.G., Hart, P.E.: Pattern Classification and Scene Analysis Part 1: Pattern Classification. Wiley & Sons, New York (2000)Google Scholar
  6. 6.
    Schikuta, E., Erhart, M.: The BANG-clustering system: Grid-based data analysis. In: Proc. of the 2nd Int. Symp. on Intelligent Data Analysis, London, Great Britain, pp. 513–524 (1997)Google Scholar
  7. 7.
    Berthold, M.R., Wiswedel, B., Patterson, D.E.: Neighborgram clustering interactive exploration of cluster neighborhoods. In: Proc. of the 2nd IEEE Int. Conf. on Data Mining, San Jose, CA, USA, pp. 581–584 (2002)Google Scholar
  8. 8.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function. Plenum Press, New York (1981)MATHGoogle Scholar
  9. 9.
    Ultsch, A., Siemon, H.P.: Kohonen’s self-organizing feature maps for exploratory data analysis. In: Proc. of the Int. Conf. on Neural Networks, Paris, France, pp. 305–308 (1990)Google Scholar
  10. 10.
    Paetz, J.: Metric rule generation with septic shock patient data. In: Proc. of the 1st IEEE Int. Conf. on Data Mining, San Jose, CA, USA, pp. 637–638 (2001)Google Scholar
  11. 11.
    Huber, K.-P., Berthold, M.R.: Building precise classifiers with automatic rule extraction. In: Proc. of the IEEE Int. Conf. on Neural Networks, Perth, Western Australia, pp. 1263–1268 (1995)Google Scholar
  12. 12.
    Paetz, J.: Monte-Carlo clustering by neuro-fuzzy classification. In: Proc. of the 1st Indian Int. Conf. on Artificial Intelligence, Hyderabad, India, pp. 66–72 (2003)Google Scholar
  13. 13.
    Silipo, R., Berthold, M.R.: Discriminative power of input features in a fuzzy model. In: Proc. of the 3rd Int. Symp. on Intelligent Data Analysis, pp. 87–98. Amsterdam, The Netherlands (1999)Google Scholar
  14. 14.
    Paetz, J.: Knowledge based approach to septic shock patient data using a neural network with trapezoidal activation functions. Artificial Intelligence in Medicine 28, 207–230 (2003)CrossRefGoogle Scholar
  15. 15.
    Metropolis, N., Ulam, S.: The Monte Carlo method. J. Amer. Stat. Assoc. 44, 335–341 (1949)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Paetz, J.: Reducing the number of neurons in radial basis function networks with dynamic decay adjustment. Neurocomputing 62, 79–91 (2004)CrossRefGoogle Scholar
  17. 17.
    Paetz, J., Arlt, B.: A neuro-fuzzy based alarm system for septic shock patients with a comparison to medical scores. In: Proc. of the 3rd Int. Symp. on Medical Data Analysis, Rome, Italy, pp. 42–52 (2002)Google Scholar
  18. 18.
    Hamker, F., Paetz, J., Thöne, S., Brause, R., Hanisch, E.: Erkennung kritischer Zustände von Patienten mit der Diagnose Septischer Schock mit einem RBF-Netz. Interner Bericht 4/00, Fachbereich Informatik, J.W. Goethe-Universität Frankfurt am Main (2000)Google Scholar
  19. 19.
    Böhm, H.J., Schneider, G.: Virtual Screening for Bioactive Molecules. Wiley VCH, Weinheim (2000)CrossRefGoogle Scholar
  20. 20.
    Schneider, G., Neidhart, W., Giller, T., Schmid, G.: Scaffold hopping by topological pharmacophore search: a contribution to virtual screening. Angewandte Chemie, International Edition 38, 2894–2895 (1999)CrossRefGoogle Scholar
  21. 21.
    Schneider, P., Schneider, G.: Collection of Bioactive Reference Compounds for Focused Library Design. QSAR & Combinatorial Science 22, 713–718 (2003)CrossRefGoogle Scholar
  22. 22.
    Gardner, M.J., Hall, N., Fung, E., White, O., Berriman, M., et al.: Genome sequence of the human malaria parasite Plasmodium falciparum. Nature 419, 498–511 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jürgen Paetz
    • 1
  1. 1.J.W. Goethe-Universität Frankfurt am MainFrankfurt am MainGermany

Personalised recommendations