# A Multi-scale Visual Analytics Approach for Exploring Biomedical Knowledge

arXiv: https://arxiv.org/abs/2109.06828 · HTML: https://mlsanity.com/paper/2109.06828/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2021.

## Built on by (most cited first, 2 of 2)

- [Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities](https://mlsanity.com/paper/2304.01311/) (2023)
- [DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs](https://mlsanity.com/paper/2601.20311/) (2026)

## Abstract (arXiv)

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.