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Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised Clustering.

Thibaut VauletGillian DivardOlivier ThaunatEvelyne LerutAleksandar SenevOlivier AubertElisabet Van LoonJasper CallemeynMarie-Paule EmondsAmaryllis Van CraenenbroeckKatrien De VusserBen SprangersMaud RabeyrinValérie DuboisDirk KuypersMaarten De VosAlexandre LoupyBart De MoorMaarten Naesens
Published in: Journal of the American Society of Nephrology : JASN (2021)
A semisupervised clustering approach for the identification of clinically meaningful novel phenotypes of kidney transplant rejection has been developed and validated. The approach has the potential to offer a more quantitative evaluation of rejection subtypes and severity, especially in situations in which the current histologic categorization is ambiguous.
Keyphrases
  • single cell
  • rna seq
  • liver failure
  • machine learning
  • respiratory failure
  • high resolution
  • drug induced
  • human health
  • extracorporeal membrane oxygenation
  • acute respiratory distress syndrome
  • mass spectrometry