biostudies-literatureUnknown6Zhang Y<h4>Introduction</h4>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.<h4>Methods</h4>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.<h4>Results and discussion</h4>Our findings not only reaffirm known targetable disease components but also spFrontiers in digital health1336050https://www.ebi.ac.uk/biostudies/studies/S-EPMC10853398biostudies-literatureA framework towards digital twins for type 2 diabetes.PMC10853398Yurkovich JTHuang SShmulevich IHood LZhang YRappaport NPflieger LQin GAguilar BfalseA framework towards digital twins for type 2 diabetes.<h4>Introduction</h4>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.<h4>Methods</h4>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.<h4>Results and discussion</h4>Our findings not only reaffirm known targetable disease components but also sp2024-01-01T00:00:00Z20242025-04-22T22:04:44.279Z2025-04-06T03:55:19.935ZS-EPMC108533983834390710.3389/fdgth.2024.1336050