| Title: | Multi-omics for subphenotyping and predicting of the ocular and systemic age-related diseases |
| Lead Institution: | Sun Yat-Sen University |
| Principal investigator: |
Application 105658
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About
Aims: The overall goal of this project is to develop an ocular-based risk assessment system for predicting aging and age-related diseases. Specific aims are: 1) To explore the relationship between eye diseases and age-related diseases. 2) To investigate the underlying mechanisms of eye diseases and systemic age-related diseases. 3) To build a deep learning system based on multi-omics techniques for predicting ageing and assessing the risks of age-related outcomes. Recent advancements in deep learning have demonstrated tremendous potential in predicting various age-related outcomes, including cardiocerebrovascular diseases, neurological diseases and even mortality, using imaging and multi-omics techniques. Since the eyes are closely associated with an aging individual's quality of life and eye examinations are easily obtainable, our attention is focused on developing a deep learning system based on eye-related multi-omics phenotypes to predict these outcomes. This project will last for 36 months. If successful, the developed deep learning system could predict the onset and progression of age-related diseases, and effective management could slow down their progression. The potential beneficiaries of this system are the aging population, which constitutes a significant portion of society.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11703 | Plasma metabolomics identifies key metabolites and improves prediction of diabetic retinopathy: development and validation across multi-national cohorts | Shaopeng Yang (+11) | 2024 | Ophthalmology |
Enabling scientific discoveries that improve human health