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Generative Adversarial Networks for Modeling Clinical Biomarker Profiles in Under-Represented Groups
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  • Rahul Nair,
  • Deen Mohan,
  • Sandra Frank,
  • Srirangaraj Setlur,
  • Venugopal Govindaraju,
  • Murali Ramanathan
Rahul Nair
University at Buffalo

Corresponding Author:rahulnai@buffalo.edu

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Deen Mohan
University at Buffalo
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Sandra Frank
University at Buffalo
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Srirangaraj Setlur
University at Buffalo
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Venugopal Govindaraju
University at Buffalo
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Murali Ramanathan
University at Buffalo
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Abstract

Background: Clinical trial simulations and pharmacometric modeling of biomarker profiles for under-represented groups are challenging because the underlying studies frequently do not have sufficient participants from these groups. Objectives: To investigate generative adversarial networks (GANs), an artificial intelligence (AI) technology that enables realistic simulations of complex patterns, for modeling clinical biomarker profiles of under-represented groups. Methods: GANs consist of generator and discriminator neural networks that operate in tandem. GAN architectures were developed for modeling univariate and joint distributions of a panel of 16 diabetes-relevant biomarkers from the National Health and Nutrition Examination Survey (NHANES), which contains laboratory and clinical biomarker data from a population-based sample of individuals of all ages, racial groups, and ethnicities. Conditional GANs were used to model biomarker profiles for race/ethnicity categories. GAN performance was assessed by comparing GAN outputs to test data. Results: The biomarkers exhibited non-normal distributions and varied in their bivariate correlation patterns. Univariate distributions were modeled with generator and discriminator neural networks consisting of two dense layers with rectified linear unit-activation. The distributions of GAN-generated biomarkers were similar to the test data distributions. The joint distributions of the biomarker panel in the GAN-generated data were dispersed and overlapped with the joint distribution of the test data as assessed by three multi-dimensional projection methods. Conditional GANs satisfactorily modeled the joint distribution of the biomarker panel in the Black, Hispanic, White, and “Other” race/ethnicity categories. Conclusions: GAN are a promising AI approach for generating virtual patient data with realistic biomarker distributions for under-represented race/ethnicity groups.
20 May 2022Submitted to British Journal of Clinical Pharmacology
23 May 2022Submission Checks Completed
23 May 2022Assigned to Editor
06 Jun 2022Reviewer(s) Assigned
10 Aug 2022Review(s) Completed, Editorial Evaluation Pending
14 Aug 2022Editorial Decision: Revise Major
23 Oct 20221st Revision Received
25 Oct 2022Assigned to Editor
25 Oct 2022Submission Checks Completed
25 Oct 2022Review(s) Completed, Editorial Evaluation Pending
05 Nov 2022Reviewer(s) Assigned
21 Nov 2022Editorial Decision: Revise Minor
23 Nov 20222nd Revision Received
24 Nov 2022Assigned to Editor
24 Nov 2022Submission Checks Completed
24 Nov 2022Review(s) Completed, Editorial Evaluation Pending
28 Nov 2022Editorial Decision: Accept