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相关概念视频

What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

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相关实验视频

Updated: May 7, 2026

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
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基因表达推断基于使用L1000数据的图形神经网络.

Tae Hyun Kim1, Harim Kim2, Hyunjin Hwang3

  • 1Department of Regulatory Science, Graduate School, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun District, Seoul 02447, South Korea.

Briefings in bioinformatics
|June 12, 2025
PubMed
概括
此摘要是机器生成的。

图形神经网络 (GNN) 通过将基因关系建模为图形来改善基因表达预测. 与传统方法相比,这种方法需要更少的数据,并提高了准确性.

关键词:
基因表达推断推断的基因表达.图表神经网络的神经网络转录组 (transcriptome) 是一个转录组.

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RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
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科学领域:

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

背景情况:

  • 基因表达特征反映了细胞状态,有助于发现功能性基因连接.
  • L1000技术为许多疾病提供了具有成本效益的基因表达数据.
  • 现有的基因表达推断方法,包括线性和深度学习模型,将数据视为向量.

研究的目的:

  • 调查基于基因表达推断的图形结构的非线性模型的有效性.
  • 将图形神经网络 (GNN) 模型的性能与传统线性和非线性非GNN模型进行比较.
  • 评估特征选择和器官信息对GNN性能的影响.

主要方法:

  • 开发和应用图形神经网络 (GNN) 模型,其中基因被表示为节点.
  • 将GNN模型性能与线性回归和其他非线性模型进行比较.
  • 评估输入特征选择策略和器官特异性数据的整合.
  • 评估基因表达推断的跨平台通用性.

主要成果:

  • 在预测基因表达值和排名方面,GNN模型显著优于线性和非线性非GNN模型.
  • 在GNN模型中,使用大约10倍少的输入信息实现了可比性能.
  • 战略输入特征选择和器官特征的包含进一步提高了GNN推断的准确性.
  • 在基因表达推断中,GNN模型展示了强大的跨平台通用性.

结论:

  • 将RNA表达数据表示为图形结构,可以有效地捕捉复杂的,非线性基因相关性.
  • 基因基因网络为基因表达特征预测提供了更准确,更有效的方法.
  • 这种以图表为基础的方法促进了对基因相互作用和细胞状态的理解.