Filtering association rules in Gene Ontology based on term specificity

Abstract

Gene Ontology (GO) is one of the most widely used ontology databases, and provides a framework to elucidate biological roles of genes or gene products by semantic analysis. GO contains terms in a structured format within three domains: biological processes, molecular functions and cellular components. GO also provides extensive annotation data across most model species. Recent studies in analysis of GO and annotation data have confronted two major challenges. The first is the increasing complexity of ontology structures. The second is the inconsistency of annotation data. In this study, we explore association rule mining to curate GO data and to achieve consistent annotations. We propose a novel specificity measure for GO terms, called VICD. This measure adopts the integrative concept of induced sub-ontologies and vectors of information content distance. By pairwise cross-ontology association rule mining, we discover specific association rules using the selected GO terms with high specificity. When we compare the VICD measure to the information content, the experimental results show that VICD scores have more positive correlations with the specificity values from other commonly referred measures than information contents. We also demonstrate that our approach generates more consistent association rules across species by using VICD scores than using information contents.