Mining Vaccine-Associated Gene Interaction Networks
Author Information
Author(s): Özgür Arzucan, Xiang Zuoshuang, Radev Dragomir R, He Yongqun
Primary Institution: University of Michigan
Hypothesis
The application of the Vaccine Ontology (VO) will enhance the prediction of IFN-γ and vaccine-mediated gene interaction networks.
Conclusion
The combined usage of biomedical ontologies and centrality-based literature mining significantly facilitates the discovery of gene interaction networks and gene-concept associations.
Supporting Evidence
- The study discovered 38 more genes and 60 more interactions using the Vaccine Ontology.
- Centrality metrics ranked 32 genes high in the VO-based IFN-γ vaccine network.
- BCG and LVS vaccines were identified as the most central vaccines in the vaccine-vaccine association network.
Takeaway
This study shows that using a special vocabulary for vaccines helps find more genes and their interactions related to immune responses.
Methodology
A literature-based discovery method using Natural Language Processing and network centrality analysis was applied to identify genes related to human IFN-γ.
Digital Object Identifier (DOI)
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