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Using probabilistic relational learning to support bronchial carcinoma diagnosis based on ion mobility spectrometry

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International Journal for Ion Mobility Spectrometry

Abstract

Ion Mobility Spectrometry (IMS) provides a means for analyzing the substances a person exhales. In this paper, we report on an approach to support early diagnosis of bronchial carcinoma based on such IMS measurements. Given the peaks in a set of ion mobility spectra, we first cluster these peaks with a modified k-means algorithm. We then apply probabilistic relational modelling and learning methods to a logical representation of the data obtained from the ion mobility spectra and the peak clusters. Markov Logic Networks and the MLN system Alchemy are employed for various modelling and learning scenarios. These scenarios are evaluated with respect to ease of use, classification accuracy, and knowledge representation aspects.

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Notes

  1. Some of the determined probabilities differed extremely, depending on the inference algorithm used for calculation (i. e. some probabilities were almost 1.0 when calculated with one algorithm, but almost 0.0 when calculated with another one). Even when just adjusting some parameter of one algorithm, some probabilities changed drastically although they ought to change only approximately.

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Authors and Affiliations

  1. Dept. of Computer Science, FernUniversität in Hagen, Hagen, Germany

    Marc Finthammer, Christoph Beierle & Jens Fisseler

  2. Dept. of Computer Science, Technische Universität Dortmund, Dortmund, Germany

    Gabriele Kern-Isberner

  3. Dept. Clinical Diagnostics, KIST Europe, Saarbrücken, Germany

    Jörg Ingo Baumbach

Authors
  1. Marc Finthammer
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  2. Christoph Beierle
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  3. Jens Fisseler
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  4. Gabriele Kern-Isberner
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  5. Jörg Ingo Baumbach
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Corresponding author

Correspondence to Marc Finthammer.

Additional information

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. Möller, B. Bödeker, and B&S Analytik for their successful cooperation, valuable contributions, and general support.

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Finthammer, M., Beierle, C., Fisseler, J. et al. Using probabilistic relational learning to support bronchial carcinoma diagnosis based on ion mobility spectrometry. Int. J. Ion Mobil. Spec. 13, 83–93 (2010). https://doi.org/10.1007/s12127-010-0042-9

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  • DOI: https://doi.org/10.1007/s12127-010-0042-9

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