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相关概念视频

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

875
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...
875
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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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...
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Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

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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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Protein Networks02:26

Protein Networks

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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,...
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相关实验视频

Updated: Jun 14, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Published on: March 1, 2024

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量子化多任务学习用于特定环境的基因网络动态的基因网络动态表示.

Han Chen1,2,3,4, Madhavan S Venkatesh1,3,5, Javier Gómez Ortega1,3

  • 1Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.

bioRxiv : the preprint server for biology
|September 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多任务学习方法,用于模拟特定环境的基因网络. 该方法通过分析数百万个单细胞转录组,有效地预测癌症等疾病的治疗点.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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科学领域:

  • 计算生物学和生物信息学
  • 基因组学和转录基因组学
  • 机器学习在生物学中的应用

背景情况:

  • 基因网络是动态的,取决于环境,而不是静态的实体.
  • 现有的模型往往无法捕捉基因调节的细微,特定于背景的性质.
  • 了解基因网络动态对于疾病建模和治疗目标识别至关重要.

研究的目的:

  • 开发一种多任务学习策略,用于生成基因网络动态的特定环境表示.
  • 从非恶性单细胞转录组创建一个基础模型,并为癌症特异性分析进行微调.
  • 在结直肠瘤微环境中识别候选治疗点.

主要方法:

  • 组装了大约1.03亿个人类单细胞转录基因组的大体.
  • 采用了两阶段的预训练策略:对非恶性细胞的基础模型,然后对癌细胞的持续学习.
  • 利用多任务学习来实现跨不同细胞类型,组织和疾病的特定语境表示,包括模型量化以提高效率.

主要成果:

  • 成功生成了特定上下文的基因网络表示.
  • 针对癌症的调整模型有效地学习了细胞状态,并预测了结直肠瘤微环境中的瘤限制因素.
  • 模型量化使得有效的微调和推理能够实现,同时保留生物见解.

结论:

  • 多任务学习为特定环境的疾病建模提供了一个强大的框架.
  • 这种方法可以产生对人类疾病候选治疗点的上下文预测.
  • 开发的战略增强了我们理解和准复杂生物系统的能力.