An exploratory study combining Virtual Reality and Semantic Web for life science research using Graph2VR
We previously described Graph2VR, a prototype that enables researchers to use virtual reality (VR) to explore and navigate through Linked Data graphs using SPARQL queries (see https://doi.org/10.1093/database/baae008). Here we evaluate the use of Graph2VR in three realistic life science use cases. The first use case visualizes metadata from large-scale multi-center cohort studies across Europe and Canada via the EUCAN Connect catalogue. The second use case involves a set of genomic data from synthetic rare disease patients, which was processed through the Variant Interpretation Pipeline and then converted into Resource Description Format for visualization. The third use case involves enriching a graph with additional information, in this case, the Dutch Anatomical Therapeutic Chemical code Ontology with the DrugID from Drugbank. These examples collectively showcase Graph2VR's potential for data exploration and enrichment, as well as some of its limitations. We conclude that the endless three-dimensional space provided by VR indeed shows much potential for the navigation of very large knowledge graphs, and we provide recommendations for data preparation and VR tooling moving forward. Database URL: https://doi.org/10.1093/database/baaf008.
Authors
- David van Enckevort (ORCID: https://orcid.org/0000-0002-2440-3993)
- K. Joeri van der Velde (ORCID: https://orcid.org/0000-0002-0934-8375)
- Morris A. Swertz (ORCID: https://orcid.org/0000-0002-0979-3401)
- Sander van den Hoek (ORCID: https://orcid.org/0009-0004-3743-650X)
- W Maassen (ORCID: https://orcid.org/0000-0001-7086-0637)
- Brenda S. Hijmans (ORCID: https://orcid.org/0000-0001-9937-2928)
- Marije A. van der Geest (ORCID: https://orcid.org/0000-0002-1919-6547)
- Alexander Kellmann (ORCID: https://orcid.org/0000-0001-6108-5552)
- Max Postema
Institutions
- University Medical Center Groningen (NL)
Publication Details
- Journal
- Database
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/database/baaf008
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- H2020 Health