GAN-WGCNA: Calculating gene modules to identify key intermediate regulators in cocaine addiction.

ABSTRACT: Understanding time-series interplay of genes is essential for diagnosis and treatment of disease. Spatio-temporally enriched NGS data contain important underlying regulatory mechanisms of biological processes. Generative adversarial networks (GANs) have been used to augment biological data to describe hidden intermediate time-series gene expression profiles during specific biological processes. Developing a pipeline that uses augmented time-series gene expression profiles is needed to provide an unbiased systemic-level map of biological processes and test for the statistical significance of the generated dataset, leading to the discovery of hidden intermediate regulators. Two analytical methods, GAN-WGCNA (weighted gene co-expression network analysis) and rDEG (rescued differentially expre

SUBMITTER: Kim T 

PROVIDER: S-EPMC11449371 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature