Advertisement

Data Mining, Modeling and Knowledge Discovery Methods for Personalised Biomedical Decision Support Systems

  • N. Kasabov
Part of the IFMBE Proceedings book series (IFMBE, volume 21)

Abstract

The paper is concerned with computational methods and systems for personalized modeling - an important topic for the future of biomedical applications. Issues discussed include: storing data and information in ontologies; personalized modeling techniques based on nearest-neighbour approach; data mining and personalized profiling; applications in cancer genetics; applications in brain-gene modelling; applications in chronic disease risk prediction.

Keywords

data mining decision support systems bioinformatics personalized modeling biomedical applications 

IV. References

  1. 1.
    N. Kasabov (2007) Evolving Connectionist Systems: The Knowledge Engineering Approach, Springer, London (www.springer.de)zbMATHGoogle Scholar
  2. 2.
    Kasabov, N., Global, local and personalised modelling and profile discovery in Bioinformatics: An integrated approach, Pattern Recognition Letters, Vol. 28, Issue 6, April 2007, 673–685CrossRefGoogle Scholar
  3. 3.
    Kasabov, N. Adaptation and Interaction in Dynamical Systems: Modelling and Rule Discovery Through Evolving Connectionist Systems, Applied Soft Computing, 2006, Volume 6, Issue 3, pages 307–322.CrossRefGoogle Scholar
  4. 4.
    Gottgtroy P., Kasabov N., Macdonell S., Evolving Ontologies for Intelligent Decision Support, Elsevier, Fuzzy Logic And The Semantic Web, Chapter 21, pp 415–439, 2006Google Scholar
  5. 5.
    Q. Song, N. Kasabov, T. Ma, M. Marshall, Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: a case study on renal function evaluation, Artificial Intelligence in Medicine, February, 2006Google Scholar
  6. 6.
    Q. Song and N. Kasabov, TNFI: A Neuro-Fuzzy Inference Method for Transductive Reasoning, IEEE Transactions on Fuzzy Systems, December, vol.13, issue 6, 2005, 799–808.CrossRefGoogle Scholar
  7. 7.
    Song, Q. and Kasabov, N. TWNFI-a transductive neuro-fuzzy inference system with weighted data normalisation for personalised modelling, Neural Networks, Vol.19, Issue 10, Dec. 2006, pp. 1591–1596zbMATHCrossRefGoogle Scholar
  8. 8.
    N. Kasabov, L. Benuskova L and Wysoski SG (2005) Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques. In: ICANN 2005, LNCS 3697, W. Duch et al (Eds), Springer-Verlag, Berlin Heidelberg, pp. 509–514.Google Scholar
  9. 9.
    Kasabov, N., V. Jain, L. Benuskova, Integrating brain-gene ontology with evolving connectionist system for modelling and discovery, Neural Networks, 21 (2008), 266–275CrossRefGoogle Scholar
  10. 10.
    L. Benuskova and N. Kasabov (2007) Computational Neurogenetic Modelling, Springer, New YorkGoogle Scholar
  11. 11.
    N Kasabov, Q Song, L Benuskoval, P Gottgtroy, V Jain, A Verma, I Havukkala, E Rush, R Pears, A Tjahjana, Y Hu, S MacDonel, Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and Bioinformatics Decision Support, in: Smolin et al (eds) Computational Intelligence in Bioinformatics, Springer, 2008Google Scholar
  12. 12.
    Y Hu, N Kasabov, Ontology-Based Framework for Personalized Diagnosis and Prognosis of Cancer Based on Gene Expression Data, ICONIP2007, Japan, 13–16 November 2007, LNCS, Part II, 4985, pp. 846–855, Springer, 2008Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • N. Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteKEDRI Auckland University of TechnologyAucklandNew Zealand

Personalised recommendations