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.