A framework towards digital twins for type 2 diabetes.

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 sp

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10853398 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature