A multi-modal vision knowledge graph of cardiovascular disease
Rjoob, K.; McGurk, K. A.; Zheng, S. L.; Curran, L.; Ibrahim, M.; Zeng, L.; Kim, V.; Tahasildar, S.; Kalaie, S.; Senevirathne, D. S.; Gifani, P.; Losev, V.; Zheng, J.; Bai, W.; de Marvao, A.; Ware, J. S.; Bender, C.; O'Regan, D. P.
Show abstract
Understanding gene-disease associations is important for uncovering pathological mechanisms and identifying potential therapeutic targets. Knowledge graphs offer a powerful solution for representing and integrating data from multiple biomedical sources, but lack individual-level information on target organ structure and function. Here we developed CardioKG, a knowledge graph integrating over 200,000 computer vision-derived cardiovascular phenotypes from biomedical images with data extracted from 18 diverse biological databases modelling over a million relationships. A variational graph auto-encoder was used to generate node embeddings from the knowledge graph, which were used as input features to predict gene-disease associations, assess druggability and propose drug repurposing strategies. The model predicted new genetic associations and therapeutic strategies for leading causes of cardiovascular disease which were also associated with improved survival. Candidate therapies included methotrexate for heart failure and gliptins for atrial fibrillation. Imaging enhanced the ability to leverage biological data for pathway discovery. These capabilities represent an important step toward using biomedical imaging to enhance graph-structured models for identifying treatable disease mechanisms.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Deep representation learning for clustering longitudinal survival data from electronic health records 95%
- Genome-wide association analysis and Mendelian randomization proteomics identify novel protein biomarkers and drug targets for primary prevention of heart failure 94%
- Biomarker panels for improved risk prediction and enhanced biological insights in patients with atrial fibrillation 94%
Similar papers in this journal
Similar papers in this journal
- Cardiovascular disease biomarkers derived from circulating cell-free DNA methylation 95%
- DeepCOMBI: Explainable artificial intelligence for the analysis and discovery in genome-wide association studies 94%
- Predicting Gene Disease Associations With Knowledge Graph Embeddings For Diseases With Curtailed Information 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.