Mining Association Rules From Disease Ontology

The Disease Ontology (DO) is standardized, controlled vocabulary that contains information about inherited, developmental and acquired human diseases. Each DO term is associated with disease concepts through an annotation process. The relevance and the specificity of DO terms are often evaluated by its Information Content (IC). An important research area focus on the analysis of annotated data with the goal to extract knowledge. For example, the analysis of annotated data using Association Rules (AR) may supply meaningful knowledge, discovering relevant associations. Classical association rules methods consider all annotation equally, do not taking into account that the DO terms have different Information Content, i.e. different relevance. This implies the generation of association rules with low IC. In this paper we presents WARDO (Weighted Association Rule mining from Disease Ontology), a methodology based on the extraction od Weighted Association Rules from the DO Ontology considering the IC of terms. To assess our methodology, we tested WARDO on DO annotation datasets. WARDO is publicly available at https://gitlab.com/giuseppeagapito/wardo.