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Semantic Subgroup Discovery and Cross-Context Linking for Microarray Data Analysis

  • Igor Mozetič
  • Nada Lavrač
  • Vid Podpečan
  • Petra Kralj Novak
  • Helena Motaln
  • Marko Petek
  • Kristina Gruden
  • Hannu Toivonen
  • Kimmo Kulovesi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7250)

Abstract

The article presents an approach to computational knowledge discovery through the mechanism of bisociation. Bisociative reasoning is at the heart of creative, accidental discovery (e.g., serendipity), and is focused on finding unexpected links by crossing contexts. Contextualization and linking between highly diverse and distributed data and knowledge sources is therefore crucial for the implementation of bisociative reasoning. In the article we explore these ideas on the problem of analysis of microarray data. We show how enriched gene sets are found by using ontology information as background knowledge in semantic subgroup discovery. These genes are then contextualized by the computation of probabilistic links to diverse bioinformatics resources. Preliminary experiments with microarray data illustrate the approach.

Keywords

Association Rule Microarray Data Analysis Subgroup Discovery Biomedical Informatics Link Discovery 
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.

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Copyright information

© The Author(s) 2012 2012

Authors and Affiliations

  • Igor Mozetič
    • 1
  • Nada Lavrač
    • 1
    • 2
  • Vid Podpečan
    • 1
  • Petra Kralj Novak
    • 1
  • Helena Motaln
    • 3
  • Marko Petek
    • 3
  • Kristina Gruden
    • 3
  • Hannu Toivonen
    • 4
  • Kimmo Kulovesi
    • 4
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.University of Nova GoricaNova GoricaSlovenia
  3. 3.National Institute of BiologyLjubljanaSlovenia
  4. 4.Department of Computer ScienceUniversity of HelsinkiFinland

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