Generalized Association Rules for Connecting Biological Ontologies

Abstract

The constantly increasing volume and complexity of available biological data requires new methods for Managing and analyzing them. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining generalized association rules connecting their categories. To select only the most important rules, we propose a new interestingness measure especially well-suited for hierarchically organized rules. To demonstrate this approach, we applied it to the bioinformatics domain and, more specifically, to the analysis of data from Gene Ontology, Cell type Ontology and GPCR databases. In this way found association rules connecting two biological ontologies can provide the user with new knowledge about underlying biological processes. The preliminary results show that produced rules represent meaningful and quite reliable associations among the ontologies and help infer new knowledge.