Abstract
Probabilistic relational modelling and learning is used for the problem of diagnosing lung cancer based on data obtained from peak clusters in ion mobility spectra. Markov Logic Networks and the MLN system Alchemy are employed for various modelling and learning scenarios which are evaluated with respect to ease of use, classification accuracy, and knowledge representation aspects.
The research reported here was partially supported by the DFG (BE 1700/7-1 and KE 1413/2-1), by Germany’s high-tech strategy funds (Project Metabolit-01SF0716) and by the European Union (Project No. 217967). We thank Dr. M. Westhoff (Lung clinic Hemer), Dr. Th. Perl (University Göttingen), B. Bödeker, and B&S Analytik for their successful cooperation, valuable contribution, and general support.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baumbach, J.I., Westhoff, M.: Ion mobility spectometry to detect lung cancer and airway infections. Spectroscopy Europe 18(6), 22–27 (2006)
Dehaspe, L., Toironen, H.: Discovery of relational association rules. In: Relational Data Mining, pp. 189–208. Springer, Heidelberg (2000)
Kersting, K., De Raedt, L.: Bayesian Logic Programming: Theory and Tool. In: An Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
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)
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)
Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20, 629–679 (1994)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1998)
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1), 107–136 (2006)
Srinivasan, A.: The Aleph Manual (2007), http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Finthammer, M., Beierle, C., Fisseler, J., Kern-Isberner, G., Möller, B., Baumbach, J.I. (2010). Probabilistic Relational Learning for Medical Diagnosis Based on Ion Mobility Spectrometry. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-14055-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14054-9
Online ISBN: 978-3-642-14055-6
eBook Packages: Computer ScienceComputer Science (R0)