A Multi-scale Visual Analytics Approach for Exploring Biomedical Knowledge

TL;DR

This paper presents a multi-scale visual analytics system designed to facilitate interactive exploration and analysis of large-scale biomedical knowledge graphs, supporting diverse therapeutic research applications.

Contribution

It introduces a flexible, scalable visual analytics framework that integrates global and local views, hierarchical layouts, and advanced search for biomedical graph analysis.

Findings

Supports graphs with over 40,000 nodes and 350,000 edges

Enables interactive exploration and querying of biomedical knowledge

Applicable across various biomedical research use cases

Abstract

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.