Mining Association Rule Bases from Integrated Genomic Data and Annotations

  • Ricardo Martinez
  • Nicolas Pasquier
  • Claude Pasquier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5488)


During the last decade, several clustering and association rule mining techniques have been applied to highlight groups of co-regulated genes in gene expression data. Nowadays, integrating these data and biological knowledge into a single framework has become a major challenge to improve the relevance of mined patterns and simplify their interpretation by biologists. GenMiner was developed for mining association rules from such integrated datasets. It combines a new nomalized discretization method, called NorDi, and the JClose algorithm to extract condensed representations for association rules. Experimental results show that GenMiner requires less memory than Apriori based approaches and that it improves the relevance of extracted rules. Moreover, association rules obtained revealed significant co-annotated and co-expressed gene patterns showing important biological relationships supported by recent biological literature.


Gene Expression Data Association Rule Gene Annotation Biological Condition Association Rule Mining 
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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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ricardo Martinez
    • 1
  • Nicolas Pasquier
    • 1
  • Claude Pasquier
    • 2
  1. 1.Laboratoire I3SUniversité de Nice Sophia-Antipolis/CNRS UMR-6070SophiaFrance
  2. 2.IDBCUniversité de Nice Sophia-Antipolis/CNRS UMR-6543, Parc ValroseNiceFrance

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