Generating full-scale libraries from small-scale libraries through the use of generative adversarial networks

• المؤلف الرئيسي: Sim, Ryan Yao Rong
• مؤلفون آخرون: Melissa Jane Fullwood
• التنسيق: Final Year Project
• اللغة: English
• منشور في: Nanyang Technological University 2024
• الموضوعات: Medicine, Health and Life Sciences; Generative adversarial networks; RNA-seq
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/175408
• المؤسسة: Nanyang Technological University

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.