In this section
@article{Rjoob:2025,
author = {Rjoob, K and McGurk, K and Zheng, S and Curran, L and Ibrahim, M and Zeng, L and Kim, V and Tahasildar, S and Kalaie, S and Senevirathne, S and Gifani, P and Zheng, J and Bai, W and de, Marvao A and Ware, J and Bender, C and O'Regan, D},
journal = {Nature Cardiovascular Research},
title = {A multi-modal vision knowledge graph of cardiovascular disease},
year = {2025}
}
TY - JOUR
AB - Understanding gene-disease associations is important for uncovering pathological mechanisms and identifying potential therapeutic targets. Knowledge graphs can represent and integrate data from multiplebiomedical sources, but lack individual-level information on target organ structure and function. Here wedevelop 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 overa million relationships. We used a variational graph auto-encoder to generate node embeddings from theknowledge graph to predict gene-disease associations, assess druggability and identify drug repurposing strategies. The model predicted genetic associations and therapeutic opportunities for leading causesof cardiovascular disease, which were associated with improved survival. Candidate therapies includedmethotrexate for heart failure and gliptins for atrial fibrillation, and the addition of imaging data enhancedpathway discovery. These capabilities support the use of biomedical imaging to enhance graph-structuredmodels for identifying treatable disease mechanisms.
AU - Rjoob,K
AU - McGurk,K
AU - Zheng,S
AU - Curran,L
AU - Ibrahim,M
AU - Zeng,L
AU - Kim,V
AU - Tahasildar,S
AU - Kalaie,S
AU - Senevirathne,S
AU - Gifani,P
AU - Zheng,J
AU - Bai,W
AU - de,Marvao A
AU - Ware,J
AU - Bender,C
AU - O'Regan,D
PY - 2025///
SN - 2731-0590
TI - A multi-modal vision knowledge graph of cardiovascular disease
T2 - Nature Cardiovascular Research
ER -