Incorporation of ontology-driven biological knowledge into cardiovascular genomics

This study presents a system that enables the incorporation of similarity knowledge extracted from the Cardiovascular Gene Ontology (CGO) into cardiovascular research. The implementation of the system is based on the combination of biological function annotations provided by the CGO for more than 4000 genes associated with cardiovascular processes and topological features encoded in the Gene Ontology (GO). Using cardiovascular-related annotations provided by CGO, term-term similarity within each of the GO hierarchies, i.e., molecular function, biological process and cellular component, is computed using three GO-driven similarity measures (Resnik's, Lin's and Jiang's metrics). These provide the foundation for the estimation of semantic similarity between cardiovascular-associated genes. The system allows users to retrieve between-gene similarity using a single query or batch query mode. This study contributes to the development of automated methods for supporting annotation tasks, such as the generation of new annotations for partially-characterized genes associated with cardiovascular disease.