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A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort.

Oscar AlcazarSung-Ting ChuangGang RenMitsunori OgiharaBobbie-Jo M Webb-RobertsonErnesto S NakayasuPeter BuchwaldMidhat H Abdulreda
Published in: bioRxiv : the preprint server for biology (2024)
Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.
Keyphrases
  • type diabetes
  • big data
  • electronic health record
  • single cell
  • insulin resistance
  • soft tissue
  • machine learning
  • risk assessment
  • metabolic syndrome
  • adipose tissue
  • artificial intelligence
  • data analysis
  • weight loss