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.
@article{arxiv.2109.06828,
title = {A Multi-scale Visual Analytics Approach for Exploring Biomedical Knowledge},
author = {Fahd Husain and Rosa Romero-Gomez and Emily Kuang and Dario Segura and Adamo Carolli and Lai Chung Liu and Manfred Cheung and Yohann Paris},
journal= {arXiv preprint arXiv:2109.06828},
year = {2021}
}