Classification of Gene Expression Data in an Ontology

  • Herman Midelfart
  • Astrid Lægreid
  • Jan Komorowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2199)

Abstract

Prediction of gene function from expression profiles is an intriguing problem that has been attempted with both unsupervised clustering and supervised learning methods. By the incorporation of prior knowledge concerning gene function, supervised methods avoid some of the problems with clustering. However, even supervised methods ignore the fact that the functional classes associated with genes are typically organized in an ontology. Hence, we introduce a new supervised method for learning in such an ontology. It is tested on both an artificial data set and a data set containing measurements from human fibroblast cells. We also give an approach for measuring the classification performance in an ontology.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Herman Midelfart
    • 1
  • Astrid Lægreid
    • 2
  • Jan Komorowski
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Science And TechnologyTrondheimNorway
  2. 2.Department of Physiology and Biomedical EngineeringNorwegian University of Science And TechnologyTrondheimNorway

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