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Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets

Abstract Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as d...

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Hoofdauteurs: Tina Rönn, Alexander Perfilyev, Nikolay Oskolkov, Charlotte Ling
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Nature Portfolio 2024-06-01
Reeks:Scientific Reports
Onderwerpen:
DNA methylation
RNA-sequencing
Genetic variation
Metabolic disease
Omics integration
Machine learning
Online toegang:https://doi.org/10.1038/s41598-024-64846-3
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