A multi-modal vision knowledge graph of cardiovascular disease
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Understanding gene–disease associations is important for uncovering pathological mechanisms and identifying potential therapeutic targets. Knowledge graphs can represent and integrate data from multiple biomedical sources, but lack individual-level information on target organ structure and function. Here we develop CardioKG, a knowledge graph that integrates over 200,000 computer vision-derived cardiovascular phenotypes from biomedical images with data extracted from 18 biological databases to model over a million relationships. We used a variational graph auto-encoder to generate node embeddings from the knowledge graph to predict gene–disease associations, assess druggability and identify drug repurposing strategies. The model predicted genetic associations and therapeutic opportunities for leading causes of cardiovascular disease, which were associated with improved survival. Candidate therapies included methotrexate for heart failure and gliptins for atrial fibrillation, and the addition of imaging data enhanced pathway discovery. These capabilities support the use of biomedical imaging to enhance graph-structured models for identifying treatable disease mechanisms.
Date Issued
2026-01-01
Date Acceptance
2025-11-10
Citation
Nature Cardiovascular Research, 2026, 5, pp.18-33
ISSN
2731-0590
Publisher
Springer Nature
Start Page
18
End Page
33
Journal / Book Title
Nature Cardiovascular Research
Volume
5
Copyright Statement
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1038/s44161-025-00757-4
Publication Status
Published
Date Publish Online
2025-12-29