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相关概念视频

Combinatorial Gene Control02:33

Combinatorial Gene Control

8.2K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.2K
Master Transcription Regulators02:23

Master Transcription Regulators

6.8K
Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
6.8K
Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

849
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
849
Epigenetic Regulation01:37

Epigenetic Regulation

3.0K
Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
3.0K
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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相关实验视频

Updated: May 14, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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通过超图生成模型推断基因调节网络.

Guangxin Su1, Hanchen Wang2, Ying Zhang3

  • 1School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia; ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS), Melbourne, VIC, Australia.

Cell reports methods
|April 12, 2025
PubMed
概括

我们介绍HyperG-VAE,这是一个新的贝叶斯深度生成模型,用于单细胞RNA测序数据分析. 这种基于超图的方法增强了基因调控网络推断和细胞聚类,为细胞异质性提供了强大的洞察力.

关键词:
CP:系统生物学 系统生物学基因监管网络 基因监管网络超图表表示学习学习学习学习.这就是 scRNA-seqq.

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科学领域:

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

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 生成的高维数据对于理解细胞异质性至关重要.
  • 从scRNA-seq数据中推断基因调节网络 (GRNs) 是具有挑战性的,因为数据的复杂性和噪音.
  • 现有的模型往往难以有效地捕捉复杂的生物关系和细胞变异.

研究的目的:

  • 开发一个新的贝叶斯深度生成模型,HyperG-VAE,用于增强scRNA-seq数据分析.
  • 改进基因调节网络 (GRNs) 的推断,并促进单细胞聚类.
  • 利用超图表征来更全面地了解细胞异质性和基因相互作用.

主要方法:

  • 开发了HyperG-VAE,这是一个超图变量自编码器模型.
  • 在细胞编码器中纳入结构方程模型,以解决细胞异质性和构建GRNs.
  • 在基因编码器中利用过度图的自我注意力来识别基因模块.
  • 采用了编码器和解码器的协同优化,以改进GRN推断和集群.

主要成果:

  • 通过基准验证,HyperG-VAE展示了改进的GRN推断,单细胞聚类和数据可视化功能.
  • 该模型有效地揭示了B细胞发育数据中的基因调节模式.
  • 基因组丰富分析证实了基因编码器在改进GRN推断方面的有效性.

结论:

  • HyperG-VAE为scRNA-seq分析和GRN构建提供了一种高效和强大的解决方案.
  • 该模型成功捕捉了细胞异质性,并识别了基因模块.
  • HyperG-VAE显示了未来在时间和多模式单细胞数据分析领域的应用潜力.