Abstract
Introduction: A digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes. Methods: Here, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic–disease relationships. Results and discussion: Our findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.
Original language | English |
---|---|
Article number | 1336050 |
Journal | Frontiers in Digital Health |
Volume | 6 |
DOIs | |
State | Published - 2024 |
Externally published | Yes |
Keywords
- digital twin
- knowledge graph
- machine learning
- precision medicine
- type 2 diabetes