Speaker
Description
Genomics, metabolomics, and proteomics offer complementary insights into cardiovascular disease (CVD) risk. In this published work, 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 that incorporating multiomics profiling into clinical practice can improve personalized risk assessments at early stages. CardiOmicScore also identifies important CVD-related proteins and metabolites, which represent promising data-driven pathways, calling for further external validation, to develop novel biomarkers and targeted therapies, facilitating precision medicine for primary prevention of CVDs.
Bibliography
Qingpeng ZHANG is an Associate Professor at the Musketeers Foundation Institute of Data Science and the Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, University of Hong Kong. He is a Senior Member of the IEEE and a Fellow of the Royal Society of Medicine. He serves as an Associate Editor for npj Digital Medicine, BMJ Mental Health, INFORMS Journal on Data Science, IEEE TCSS, and IEEE TITS. Dr. Zhang’s research centers on the creation of knowledge-enhanced, AI-driven predictive decision analytics methods. These methods aim to dissect high-dimensional biological, clinical, and behavioral data to contribute to drug discovery, precision medicine, and public health. His work has appeared in journals such as Nature Human Behaviour, Nature Communications, PNAS, and MIS Quarterly, and has been highlighted in media outlets such as The Washington Post, The New York Times, The Guardian, and Ming Pao.
