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相关概念视频

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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Overview of Cell-Matrix Interactions01:24

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The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
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Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
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相关实验视频

Updated: Jul 13, 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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解码功能细胞-细胞通信事件通过空间转录学上的多视图学习.

Haochen Li1, Tianxing Ma2, Minsheng Hao2

  • 1School of Medicine, Tsinghua University, Beijing 100084, China.

Briefings in bioinformatics
|October 12, 2023
PubMed
概括
此摘要是机器生成的。

通过整合体-受体对和空间转录学,HoloNet解码功能细胞-细胞通信事件 (FCE). 这种深度学习方法揭示了FCE如何影响基因表达和细胞表型,在识别关键通信通路方面表现优于现有的方法.

关键词:
细胞细胞通信 细胞细胞通信功能通信事件 功能通信事件多视图图表学习多视图图表学习空间转录学 空间转录学

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科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 基因组学就是基因组学.

背景情况:

  • 细胞与细胞之间的沟通对于生物过程至关重要,通过连接体-受体 (LR) 配对进行介导.
  • 鉴定驱动目标响应的特定功能通信事件 (FCE) 由于复杂的相互作用,仍然具有挑战性.
  • 现有的方法难以分辨影响FCE的因素及其下游基因表达效应.

研究的目的:

  • 开发一种新的计算方法来解码FCE及其目标基因关系.
  • 利用空间转录组数据和深度学习来理解细胞间的通信.
  • 确定特定的FCE及其对复杂微环境中的细胞表型的影响.

主要方法:

  • 开发了HoloNet,这是一个集成LR对,细胞类型空间分布和基因表达的深度学习模型.
  • 模拟细胞-细胞通信事件 (CEs) 作为一个多视图网络.
  • 采用基于注意力的图形学习方法来预测基因表达和解释FCEs.

主要成果:

  • HoloNet成功地解码了乳腺癌和肝癌空间转录基因数据集中的FCE.
  • 该方法有效地解开了LR信号和细胞类型对生物过程的贡献.
  • 通过模拟实验,HoloNet在与现有方法相比,在配体-受体优先排序方面表现出卓越的性能.

结论:

  • HoloNet提供了一种强大的工具,用于剖析细胞间的通信场景.
  • 该方法准确地识别了塑造细胞表型的重要FCE.
  • 在特定的微环境中,HoloNet提供了一种强大的方法来理解基因调节.