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相关概念视频

What is Gene Expression?01:42

What is Gene Expression?

197.2K
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...
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What is Gene Expression?01:36

What is Gene Expression?

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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...
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Chromatin Position Affects Gene Expression02:35

Chromatin Position Affects Gene Expression

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...
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mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability
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相关实验视频

Updated: Feb 15, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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一个新的多目标优化框架使用NSGA-II用于基因共表达网络推断.

Behnam Aghajan1, Mohammad Reza Ghaemi1, Ali M Mosammam2

  • 1Department of Mathematics. Faculty of Sciences, University of Zanjan, Zanjan, Iran.

Computational biology and chemistry
|February 13, 2026
PubMed
概括
此摘要是机器生成的。

我们使用非主导排序基因算法II (NSGA-II) 开发了一种新的多目标优化方法,以改进基因共表达网络 (GCN). 这种方法提高了网络可靠性和转录基因数据分析的生物相关性.

关键词:
阿拉克尼 (Aracne) 是一个城市.基因共同表达网络是基因的共同表达网络.多目标优化多目标优化在NSGA-II中,NSGA-II是最重要的.在WGCNA中,WGCNA是WGCNA.

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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
  • 系统生物学 系统生物学

背景情况:

  • 基因共同表达网络 (GCNs) 对于从转录基因数据中理解基因功能和通路至关重要.
  • 杂的生物数据往往导致不可靠的GCN,具有虚假的连接和不现实的结构.
  • 现有的方法很难有效地平衡网络属性,如稀疏性和模块化.

研究的目的:

  • 引入一种新的多目标优化框架,以改进GCN中的边缘选择.
  • 为了提高从转录基因数据中获得的GCN的可靠性和生物可信性.
  • 同时优化多个网络特性,包括稀疏性,模块化和无尺度拓.

主要方法:

  • 使用差异稳定转换 (VST) 进行RNA-seq数据规范化.
  • 雇佣的斯皮尔曼等级相关性,以获得强大的协同表达估计.
  • 综合非主导排序基因算法II (NSGA-II) 用于多目标网络优化.
  • 集成的排列测试和引导重新抽样,用于显著性和稳定性评估.

主要成果:

  • 与WGCNA和ARACNE相比,提出的基于NSGA-II的方法产生了较少和更模块化的GCN.
  • 优化的网络在微阵列和RNA-seq数据集中表现出更好的对无尺度网络属性的坚持.
  • 该方法在异质转录组数据集 (GSE10245和GSE102349) 上显示出强大的性能.

结论:

  • 优化驱动的策略为构建高质量的GCN提供了一个强大的方法.
  • 这种方法为整合性基因组研究和生物标志物发现提供了重大进展.
  • 该框架有可能改善复杂疾病机制的建模.