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Multiparameter phenotyping of platelets and characterization of the effects of agonists using machine learning.

Ami VadgamaJames BootNicola DarkHarriet E AllanCharles A MeinPaul C ArmstrongTimothy D Warner
Published in: Research and practice in thrombosis and haemostasis (2024)
Our approach provides a powerful phenotypic assay coupled with robust bioinformatic and machine learning workflows for deep analysis of platelet subpopulations. Cleavable receptors, glycoprotein VI and CD42b, contribute to defining shared and unique subpopulations. This adoptable, low-volume approach will be valuable in deep characterization of platelets in disease.
Keyphrases
  • machine learning
  • high throughput
  • artificial intelligence
  • flow cytometry
  • big data
  • red blood cell