Contribution to Gene Expression Data Analysis by Means of Set Pattern Mining

  • Ruggero G. Pensa
  • Jérémy Besson
  • Céline Robardet
  • Jean-François Boulicaut
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3848)


One of the exciting scientific challenges in functional genomics concerns the discovery of biologically relevant patterns from gene expression data. For instance, it is extremely useful to provide putative synexpression groups or transcription modules to molecular biologists. We propose a methodology that has been proved useful in real cases. It is described as a prototypical KDD scenario which starts from raw expression data selection until useful patterns are delivered. It has been validated on real data sets. Our conceptual contribution is (a) to emphasize how to take the most from recent progress in constraint-based mining of set patterns, and (b) to propose a generic approach for gene expression data enrichment. Doing so, we survey our algorithmic breakthrough which has been the core of our contribution to the IST FET cInQ project.


Gene Expression Data Association Rule Formal Concept Boolean Matrix Monotonic Constraint 
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

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruggero G. Pensa
    • 1
  • Jérémy Besson
    • 1
    • 2
  • Céline Robardet
    • 3
  • Jean-François Boulicaut
    • 1
  1. 1.INSA Lyon, LIRIS CNRS UMR 5205VilleurbanneFrance
  2. 2.UMR INRA/INSERM 1235LyonFrance
  3. 3.INSA Lyon, PRISMAVilleurbanneFrance

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