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Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.

Oscar AlcazarLuis F HernandezErnesto S NakayasuCarrie D NicoraCharles AnsongMichael J MuehlbauerJames R BainCiara J MyerSanjoy K BhattacharyaPeter BuchwaldMidhat H Abdulreda
Published in: Biomolecules (2021)
Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.
Keyphrases
  • single cell
  • type diabetes
  • genome wide
  • machine learning
  • deep learning
  • cardiovascular disease
  • insulin resistance
  • glycemic control
  • gene expression
  • adipose tissue
  • risk assessment
  • dna methylation
  • weight loss