Abstract
AbstractThe notion of rules is very popular and appears in different flavors, for example as association rules in data mining or as functional (or multivalued) dependencies in databases. Their syntax is the same but their semantics widely differs. In this article, we focus on semantics for which Armstrong’s axioms are sound and complete. In this setting, we propose a unifying framework in which any ”well-formed” semantics for rules may be integrated. We do not focus on the underlying data mining problems posed by the discovery of rules, rather we prefer to emphasize the expressiveness of our contribution in a particular domain of application: the understanding of gene regulatory networks from gene expression data. The key idea is that biologists have the opportunity to choose – among some predefined semantics – or to define the meaning of their rules which best fits into their requirements. Our proposition has been implemented and integrated into an existing open-source system named MeV of the TIGR environment devoted to microarray data interpretation.KeywordsGene Expression DataAssociation RuleFunctional DependencyGene Regulatory NetworkMicroarray Data AnalysisThese 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.
Published Version
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