Generative adversarial networks for modelling clinical biomarker profiles with race/ethnicity.

Journal: British journal of clinical pharmacology
Published Date: Dec 20, 2022

Abstract

AIMS: Modelling biomarker profiles for under-represented race/ethnicity groups are challenging because the underlying studies frequently do not have sufficient participants from these groups. The aim was to investigate generative adversarial networks (GANs), an artificial intelligence technology that enables realistic simulations of complex patterns, for modelling clinical biomarker profiles of under-represented groups.

Authors

  • Rahul Nair
    Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
  • Deen Dayal Mohan
    Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
  • Sandra Frank
    Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA.
  • Srirangaraj Setlur
    Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
  • Venugopal Govindaraju
    Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA.
  • Murali Ramanathan
    Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA.

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