ArticleNature cardiovascular research2026
A multimodal vision knowledge graph of cardiovascular disease.
Article in Nature cardiovascular research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Next-Generation Bone Marrow Cell Therapies for Cardiac Repair: Integrating Gene Therapy and Bioengineering to Enhance Therapeutic Potency.Journal of cardiovascular translational research · 2026Review
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- AI-Enabled Precision Echocardiography: Toward Personalized Cardiovascular Care.Diagnostics (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.