Analysis of vaccine-related networks using semantic medline and the vaccine ontology

A major challenge in the vaccine research has been to identify important vaccine-related networks and logically explain the results. In this paper, we showed that networkbased analysis of vaccine-related networks can discover the underlying structure information consistent with that captured by the Vaccine Ontology and propose new hypotheses for vaccine disease or gene associations. First, a vaccine-vaccine network was inferred using a bipartite network projection strategy on the vaccine-disease network extracted from the Semantic MEDLINE database. In total, 76 vaccines and 573 relationships were identified to construct the vaccine network. The shortest paths between all pairs of vaccines were calculated within the vaccine network. The correlation between the shortest paths of vaccine pairs and their semantic similarities in the Vaccine Ontology was then investigated. Second, a vaccinegene network was also constructed, in which several important vaccine-related genes were identified. This study demonstrated that a combinatorial analysis using literature knowledgebase, semantic technology, and ontology is able to reveal unidentified important knowledge critical to biomedical research and public health and generate testable hypotheses for future experimental verification.