Gaussian Logic for Predictive Classification

  • Ondřej Kuželka
  • Andrea Szabóová
  • Matěj Holec
  • Filip Železný
Conference paper

DOI: 10.1007/978-3-642-23783-6_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)
Cite this paper as:
Kuželka O., Szabóová A., Holec M., Železný F. (2011) Gaussian Logic for Predictive Classification. In: Gunopulos D., Hofmann T., Malerba D., Vazirgiannis M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science, vol 6912. Springer, Berlin, Heidelberg

Abstract

We describe a statistical relational learning framework called Gaussian Logic capable to work efficiently with combinations of relational and numerical data. The framework assumes that, for a fixed relational structure, the numerical data can be modelled by a multivariate normal distribution. We demonstrate how the Gaussian Logic framework can be applied to predictive classification problems. In experiments, we first show an application of the framework for the prediction of DNA-binding propensity of proteins. Next, we show how the Gaussian Logic framework can be used to find motifs describing highly correlated gene groups in gene-expression data which are then used in a set-level-based classification method.

Keywords

Statistical Relational Learning Proteomics Gene Expression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ondřej Kuželka
    • 1
  • Andrea Szabóová
    • 1
  • Matěj Holec
    • 1
  • Filip Železný
    • 1
  1. 1.Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic

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