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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Jun 20, 2025

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
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Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

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TabDEG:基于特征提取和深度学习框架的RNA-seq数据对差异表达基因进行分类.

Sifan Feng1, Zhenyou Wang1, Yinghua Jin1

  • 1School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, Guangdong, China.

PloS one
|July 22, 2024
PubMed
概括

这项研究介绍了TabDEG,这是一种新的深度学习模型,它使用数据增强来准确识别小RNA-Seq数据集中的差异表达基因 (DEG),改进癌症研究.

科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.

背景情况:

  • 鉴定差异表达基因 (DEGs) 的传统方法由于分布假设而与小样本大小作斗争,导致高错误率.
  • 深度学习 (DL) 为分析基因表达数据提供了一个有希望的替代方案,但在RNA-Seq数据的标记和样本大小方面仍然存在挑战.
  • 数据增强 (DA) 可以从有限的数据中生成有价值的伪值,提高特征提取,而无需大量成本.

研究的目的:

  • 开发一个强大的模型,TabDEG,将数据增强 (DA) 与深度学习 (DL) 框架集成,以改进DEG识别.
  • 从基因表达数据准确预测DEG及其调节方向 (上调/下调).
  • 解决传统模型在高维,小样本大小数据集中的局限性,特别是在癌症基因组学中.

主要方法:

  • 拟议的TabDEG模型结合了DA和DL的表格数据建模.
  • 利用了来自癌症基因组图谱 (TCGA) 数据库的基因表达数据.
  • 将TabDEG的性能与五种现有的DEG识别方法进行比较.

主要成果:

  • 与对应方法相比,TabDEG表现出高灵敏度和低错误分类率.
  • 该模型有效地增强了用于分类高维,小样本大小数据集的数据特征.

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  • 从TabDEG预测的DEG显着映射到重要的基因本体学术语和与癌症相关的途径.

    • 结论:

    • TabDEG是一种强大而有效的方法,用于在具有挑战性的小样本大小数据集中识别DEG.
    • 在癌症研究中,DA和DL的整合为分析RNA-Seq数据提供了一个强大的方法.
    • TabDEG有助于发现与癌症发展相关的生物相关基因和途径.