A Multi-scale Visual Analytics Approach for Exploring Biomedical Knowledge
This approach addresses the problem of scalable biomedical data exploration for researchers, though it appears incremental as it builds on existing visual analytics techniques.
The paper tackles the challenge of exploring and analyzing large-scale biomedical knowledge graphs by introducing a multi-scale visual analytics approach that integrates global and local views, hierarchical layouts, and interactive tools, enabling researchers to handle graphs with over 40,000 nodes and 350,000 edges.
This paper describes an ongoing multi-scale visual analytics approach for exploring and analyzing biomedical knowledge at scale.We utilize global and local views, hierarchical and flow-based graph layouts, multi-faceted search, neighborhood recommendations, and document visualizations to help researchers interactively explore, query, and analyze biological graphs against the backdrop of biomedical knowledge. The generality of our approach - insofar as it re-quires only knowledge graphs linked to documents - means it can support a range of therapeutic use cases across different domains, from disease propagation to drug discovery. Early interactions with domain experts support our approach for use cases with graphs with over 40,000 nodes and 350,000 edges.