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An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study.

Roderick C SliekerMagnus M MünchLouise A DonnellyGerard A BoulandIulian DraganDmitry KuznetsovPetra J M EldersGuy A RutterMark IbbersonEwan R PearsonLeendert M 't HartMark A van de WielJoline Wilhelma Johanna Beulens
Published in: Diabetologia (2024)
Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .
Keyphrases
  • machine learning
  • type diabetes
  • big data
  • cardiovascular disease
  • electronic health record
  • artificial intelligence
  • single cell
  • glycemic control
  • deep learning
  • adipose tissue