Generating full-scale libraries from small-scale libraries through the use of generative adversarial networks
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 leg...
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| 格式: | Final Year Project |
| 語言: | English |
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Nanyang Technological University
2024
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| 在線閱讀: | https://hdl.handle.net/10356/175408 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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. |
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