Advertisement

Biomedical Diagnosis Based on Ion Mobility Spectrometry – A Case Study Using Probabilistic Relational Modelling and Learning

  • Marc Finthammer
  • Ryszard Masternak
  • Christoph Beierle
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 300)

Abstract

Aiming at providing a non-invasive and easy-to-use method for the early detection of bronchial carcinoma, it has been proposed to apply ion mobility spectrometry (IMS) to the breath a person exhales. Extending previous work using such IMS data, we report on a case study using methods of probabilistic relational modelling and learning. By taking additional features of an IMS measurement into account and using refined clustering and modelling methods, inference accuracy is increased.

Keywords

Drift Time Inductive Logic Programming Brier Score Peak Cluster Markov Network 
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.
    Baumbach, J.I., Westhoff, M.: Ion mobility spectometry to detect lung cancer and airway infections. Spectroscopy Europe 18(6), 22–27 (2006)Google Scholar
  2. 2.
    Brier, G.W.: Verification of forecasts expressed in terms of probability. Monthly Weather Review 78(1), 1–3 (1950)CrossRefGoogle Scholar
  3. 3.
    Finthammer, M., Beierle, C., Fisseler, J., Kern-Isberner, G., Baumbach, J.I.: Using probabilistic relational learning to support bronchial carcinoma diagnosis based on ion mobility spectrometry. International Journal for Ion Mobility Spectrometry 13, 83–93 (2010)CrossRefGoogle Scholar
  4. 4.
    Finthammer, M., Beierle, C., Fisseler, J., Kern-Isberner, G., Möller, B., Baumbach, J.I.: Probabilistic Relational Learning for Medical Diagnosis Based on Ion Mobility Spectrometry. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. CCIS, vol. 80, pp. 365–375. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press (2007)Google Scholar
  6. 6.
    Kok, S., Singla, P., Richardson, M., Domingos, P., Sumner, M., Poon, H., Lowd, D., Wang, J.: The Alchemy System for Statistical Relational AI: User Manual. Department of Computer Science and Engineering. University of Washington (2008)Google Scholar
  7. 7.
    Lindeberg, T.: Scale-space. In: Wah, B.W. (ed.) Wiley Encyclopedia of Computer Science and Engineering. John Wiley & Sons, Inc. (2008)Google Scholar
  8. 8.
    MacQueen, J.B.: Some methods of classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  9. 9.
    Masternak, R.: Application of probabilistic relational learning for the analysis of multidimensional, spectrometric data. Master Thesis, Dept. of Computer Science, FernUniversität in Hagen, Germany (2011) (in German) Google Scholar
  10. 10.
    Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20, 629–679 (1994)CrossRefGoogle Scholar
  11. 11.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann (1988)Google Scholar
  12. 12.
    Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1), 107–136 (2006)CrossRefGoogle Scholar
  13. 13.
    Srinivasan, A.: The Aleph Manual (2007), www.comlab.ox.ac.uk/activities/machinelearning/Aleph/
  14. 14.
    Yu, W., Li, X., Liu, J., Wu, B., Williams, K.R., Zhao, H.: Multiple peak alignment in sequential data analysis: A scale-space-based approach. IEEE/ACM Trans. Comput. Biology Bioinform. 3(3), 208–219 (2006)CrossRefGoogle Scholar
  15. 15.
    Zhou, X.-H., McClish, D.K., Obuchowski, N.A.: Statistical Methods in Diagnostic Medicine, 2nd edn. Wiley, Hoboken (2011)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marc Finthammer
    • 1
  • Ryszard Masternak
    • 1
  • Christoph Beierle
    • 1
  1. 1.Dept. of Computer ScienceFernUniversität in HagenHagenGermany

Personalised recommendations