Mining cross-ontology weighted association rules between GO and HPO

Abstract

An ontology is a framework for describing domain-specific knowledge in a structured format. It is comprised of a set of terms as nodes and a set of relationships between terms as directed edges to form a directed acyclic graph. Gene Ontology (GO) and Human Phenotype Ontology (HPO) are widely referred biological and biomedical ontology databases. They also provide extensive annotations of human genes. Recent studies have applied association rule mining techniques to these ontology and annotation data in order to predict cellular functions of genes and determine gene-to-disease relations. We present a new approach to extract pairwise association rules between specific terms. Our approach selects significant, specific rules by weighted measures of support, confidence and coverage which incorporate weighting terms by integration of the ontology structure and their information contents. In our experiment, the cross-ontology association rules generated from GO and HPO by our approach were compared to those by two previous methods. The results show that our approach discovers association rules between more specific terms than the previous methods.