AI-based multiomics profiling reveals complementary omics contributions to personalized prediction of cardiovascular disease
City University of Hong Kong · Shanghai Institute of Computing Technology · +12 more institutions
Abstract
Genomics, metabolomics, and proteomics offer complementary insights into cardiovascular disease (CVD) risk. Leveraging UK Biobank data, we introduce the CardiOmicScore, a multitask deep learning framework, to learn disease-specific proteomic (ProScore) and metabolomic (MetScore) risk scores for the six most common CVDs by profiling 2920 proteins and 168 metabolites. Experiments demonstrate that ProScore and MetScore are strong sole CVD risk predictors (C-index range: 0.69–0.82 for ProScore and 0.64–0.74 for MetScore), and can significantly enhance risk prediction across CVDs up to 15 years prior to disease onset when combined with clinical data, increasing the C-index by 0.005–0.102. These findings suggest…
Citation impact
- FWCI
- 52.17
- Percentile
- 100%
- References
- 69
Authors
8- YLYan Luo
City University of Hong Kong, Shanghai Institute of Computing Technology, University of Hong Kong
- NZNan Zhang
Second Hospital of Tianjin Medical University, Tianjin Medical University
- JYJiannan Yang
Nanjing University, University of Hong Kong
- MCMengyao Cui
University of Hong Kong
- KKKelvin K. F. Tsoi
Chinese University of Hong Kong, Hong Kong Jockey Club
Topics & keywords
- Biobank
- Disease
- Profiling (computer programming)
- Omics
- Precision medicine
- Personalized medicine
- Proteomics
- Biomarker discovery