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ORIGINAL RESEARCH article

Front. Digit. Health
Sec. Health Informatics
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1336050
This article is part of the Research Topic Digital Twins in Medicine - Transition from Theoretical Concept to Tool used in Everyday Care View all 3 articles

A framework towards digital twins for type 2 diabetes

Yue Zhang Yue Zhang 1GUANGRONG QIN GUANGRONG QIN 1Boris Aguilar Boris Aguilar 1Noa Rappaport Noa Rappaport 1,2James Yurkovich James Yurkovich 2,3Lance Pflieger Lance Pflieger 2,3Sui Huang Sui Huang 1Leroy Hood Leroy Hood 1,2,3Ilya Shmulevich Ilya Shmulevich 1*
  • 1 Institute for Systems Biology (ISB), Seattle, Washington, United States
  • 2 Center for Phenomic Health, Buck Institute for Research on Aging, Novato, California, United States
  • 3 Phenome Health, Seattle, California, United States

The final, formatted version of the article will be published soon.

    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. Herein, 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. Further, knowledge graphs were employed to elucidate and contextualize multiomic-disease relationships. 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 seamlessly incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.

    Keywords: Digital Twin, type 2 diabetes, knowledge graph, machine learning, precision medicine

    Received: 09 Nov 2023; Accepted: 15 Jan 2024.

    Copyright: © 2024 Zhang, QIN, Aguilar, Rappaport, Yurkovich, Pflieger, Huang, Hood and Shmulevich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ilya Shmulevich, Institute for Systems Biology (ISB), Seattle, 98109, Washington, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.