Generative Adversarial Networks for Modeling Clinical Biomarker
Profiles in Under-Represented Groups
Rahul Nair 1, Deen Dayal Mohan 1,
Sandra Frank 2, Srirangaraj Setlur1, Venugopal Govindaraju 1 and
Murali Ramanathan 2
1 Department of Computer Science and Engineering,
University at Buffalo, The State University of New York, Buffalo, NY,
USA.
2 Department of Pharmaceutical Sciences, University at
Buffalo, The State University of New York, Buffalo, NY, USA.
Corresponding Author: Murali Ramanathan, 355 Pharmacy Building,
Department of Pharmaceutical Sciences, State University of New York,
Buffalo, Buffalo, NY 14214-8033. (716)-645-4846 and FAX 716-829-6569.
E-mail Murali@Buffalo.Edu.
ORCID: 0000-0002-9943-150X.
Running Head : Generative adversarial networks for biomarkers
Keywords: Artificial intelligence, AI, generative adversarial
networks, pharmacometrics.
Word Count: Title: 100 Characters, Running Head: 47 characters,
Abstract: 250 words, Introduction to Discussion: 3670 words. References:
25. Tables: 1. Figures: 5.
Data availability statement: The data that support the findings
of this study are openly available in NHANES at
https://www.cdc.gov/nchs/nhanes/index.htm, reference number 16.
Author Contributions: Rahul Nair – Conducted experiments, data
analysis, manuscript preparation. Sandra Frank – Obtained data, data
analysis, manuscript preparation. Deen Dayal Mohan – Designed
experiments, data analysis, manuscript preparation. Srirangaraj Setlur
– Study concept and design, data analysis, manuscript preparation. Venugopal Govindaraju – Study oversight, manuscript review. Murali Ramanathan –
Study concept and design, data analysis, manuscript preparation.
Ethics approval statement: Not applicable
Patient consent statement: Not applicable
Permission to reproduce material from other sources: Not
applicable
Clinical trial registration: Not applicable
Conflict of Interest Disclosure: Rahul Nair, Sandra Frank, and
Deen Dayal Mohan have no conflicts. Srirangaraj Setlur and Venugopal Govindaraju received unrelated research funding from the National
Science Foundation, United States Postal Service, and the Intelligence
Advanced Research Projects Activity agencies. Murali Ramanathan received research funding from the National
Science Foundation, Otsuka Pharmaceuticals, and the National Institutes
of Health.
Funding: Support from Grant MS190096 from the Department of
Defense Multiple Sclerosis Research Program for the Office of the
Congressionally Directed Medical Research Programs (CDMRP) to the
Ramanathan laboratory is gratefully acknowledged.
Confidentiality: Use of the information in this manuscript for
commercial, non-commercial, research or purposes other than peer review
not permitted prior to publication without expressed written permission
of the author.