A framework towards digital twins for type 2 diabetes.

Autor:	Zhang Y; Institute for Systems Biology, Seattle, WA, United States., Qin G; Institute for Systems Biology, Seattle, WA, United States., Aguilar B; Institute for Systems Biology, Seattle, WA, United States., Rappaport N; Institute for Systems Biology, Seattle, WA, United States.; Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, United States., Yurkovich JT; Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, United States.; Phenome Health, Seattle, WA, United States., Pflieger L; Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, United States.; Phenome Health, Seattle, WA, United States., Huang S; Institute for Systems Biology, Seattle, WA, United States., Hood L; Institute for Systems Biology, Seattle, WA, United States.; Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, United States.; Phenome Health, Seattle, WA, United States., Shmulevich I; Institute for Systems Biology, Seattle, WA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in digital health [Front Digit Health] 2024 Jan 26; Vol. 6, pp. 1336050. Date of Electronic Publication: 2024 Jan 26 (Print Publication: 2024).
DOI:	10.3389/fdgth.2024.1336050
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (© 2024 Zhang, Qin, Aguilar, Rappaport, Yurkovich, Pflieger, Huang, Hood and Shmulevich.)
Databáze:	MEDLINE
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