Generating full-scale libraries from small-scale libraries through the use of generative adversarial networks - Sim, Ryan Yao Rong (Nanyang Technological University, 2024)
RNA sequencing (RNA-seq) is a useful tool for investigating gene expression and transcriptomic landscapes in various biological contexts. (Wang. Z et al.,2010) However, conducting large-scale RNA-seq experiments can be expensive, resource-intensive, and difficult to obtain as sometimes there are legal and ethical problems involved if human-identifiable data is involved. (Ai, X.,2023) To overcome this issue, researchers have explored the possibility of creating large-scale RNA-seq libraries from smaller datasets using Generative Adversarial Networks (GANs). The aim is to determine whether small-scale libraries are able to encapsulate the core elements of large-scale RNA-seq libraries and to determine whether GANs are able to generate synthetic data that mimics the original dataset and preserves the biological correlation between the samples in the datasets.