Ontology-supported research on vaccine efficacy, safety and integrative biological networks

Abstract

While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including Vaccine Ontology, Ontology of Adverse Events and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network (‘OneNet’) Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.

Keywords::

• adverse event
• data mining
• interaction network
• literature mining
• meta-analysis
• ontology
• theory
• vaccine
• vaccine efficacy
• vaccine safety

Acknowledgements

The author appreciates three anonymous reviewers for their insightful comments.

Financial & competing interests disclosure

This work was supported by NIH-NIAID grant R01AI081062. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Key issues

• Vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction, literature mining and bioinformatic analysis.

• The community-based Vaccine Ontology supports vaccine data integration and enhances vaccine literature mining.

• The Ontology of Adverse Events (OAE) represents various AEs and supports classification and data analysis of clinically reported AE.

• The Ontology of Vaccine AEs (OVAE) represents known vaccine AEs as a knowledge base and supports automated query and analysis of know vaccine AEs.

• To support vaccine data integration, minimal information about vaccine research can be generated and formulated with ontology support.

• Ontology-based statistical meta-analysis will allow more advanced meta-analysis of the relations between different variables using data from different studies.

• An OBO Foundry ontologies-based linked open data (LOD) (OBO-LOD) is proposed for biological data representation and integration, and its application in the vaccine domain (LODV) will support better vaccine data standardization and its integration and sharing with other research domains.

• The author proposes to integrate and extend existing theories to form the ‘OneNet’ Theory of Life for better understanding of fundamental biological interaction networks including vaccine immune network mechanism.

• To apply the ‘OneNet’, the author proposes to develop ontologies (e.g., human interaction network ontology) to represent non-redundant interaction pathways by integrating knowledge collected in databases and reading/assembling experimentally verified data from published literature reports.

• Ontology-based software programs are being developed to support ontology generation and applications.

Notes