A multimodal vision knowledge graph of cardiovascular disease.

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

SUBMITTER: Rjoob K 

PROVIDER: S-EPMC12811117 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature