GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis

Authors

Michael R Waters, Washington University School of Medicine in St. Louis
Matthew Inkman, Washington University School of Medicine in St. Louis
Kay Jayachandran, Washington University School of Medicine in St. Louis
Roman M Kowalchuk, Mayo Clinic
Clifford Robinson, Washington University School of Medicine in St. Louis
Julie K Schwarz, Washington University School of Medicine in St. Louis
S Joshua Swamidass, Washington University School of Medicine in St. Louis
Obi L Griffith, Washington University School of Medicine in St. Louis
Jeffrey J Szymanski, Washington University School of Medicine in St. Louis
Jin Zhang, Washington University School of Medicine in St. Louis

Journal

Patterns

Publication Date

2-9-2024

Volume

5

Issue

2

First Page

100910

Document Type

Open Access Publication

DOI

10.1016/j.patter.2023.100910

Rights and Permissions

Waters MR, Inkman M, Jayachandran K, Kowalchuk RM, Robinson C, Schwarz JK, Swamidass SJ, Griffith OL, Szymanski JJ, Zhang J. GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis. Patterns (N Y). 2024 Jan 8;5(2):100910. doi: 10.1016/j.patter.2023.100910. Copyright 2023 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Recommended Citation

Waters, Michael R; Inkman, Matthew; Jayachandran, Kay; Kowalchuk, Roman M; Robinson, Clifford; Schwarz, Julie K; Swamidass, S Joshua; Griffith, Obi L; Szymanski, Jeffrey J; and Zhang, Jin, "GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis." Patterns. 5, 2. 100910 (2024).
https://digitalcommons.wustl.edu/oa_4/4538

Department

ICTS (Institute of Clinical and Translational Sciences)

Additional Links

Supplemental material is available for this article at publisher site.