{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["6"],"submitter":["Zhang Y"],"pubmed_abstract":["<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 sp"],"journal":["Frontiers in digital health"],"pagination":["1336050"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10853398"],"repository":["biostudies-literature"],"pubmed_title":["A framework towards digital twins for type 2 diabetes."],"pmcid":["PMC10853398"],"pubmed_authors":["Yurkovich JT","Huang S","Shmulevich I","Hood L","Zhang Y","Rappaport N","Pflieger L","Qin G","Aguilar B"],"additional_accession":[]},"is_claimable":false,"name":"A framework towards digital twins for type 2 diabetes.","description":"<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 sp","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-22T22:04:44.279Z","creation":"2025-04-06T03:55:19.935Z"},"accession":"S-EPMC10853398","cross_references":{"pubmed":["38343907"],"doi":["10.3389/fdgth.2024.1336050"]}}