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相关概念视频

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What is Cell Signaling?

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Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate to respond to the environment.
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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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Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
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Cells respond to many types of information, often through receptor proteins positioned on the membrane. They respond to chemical signals, such as hormones, neurotransmitters, and other signaling molecules, initiating a series of molecular reactions to produce an appropriate response. This is called signal transduction. Cells also coordinate different responses elicited by the same signaling molecule via mediators, allowing molecular cross-talk.
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Immunoglobulin-like cell adhesion molecules or Ig-CAMs are a versatile group of cell surface glycoproteins belonging to the immunoglobulin protein superfamily. Ig-CAMs possess the characteristic immunoglobulin protein domains and other domains such as the fibronectin type III domain. The Ig domains are glycosylated to varying degrees in different Ig-CAMs.
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相关实验视频

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A Gradient-generating Microfluidic Device for Cell Biology
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基于梯度增强神经网络和可解释的增强机器来识别潜在的连接体-受体相互作用,用于细胞间通信分析.

Lihong Peng1, Pengfei Gao1, Wei Xiong1

  • 1College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.

Computers in biology and medicine
|February 17, 2024
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概括

CellGiQ是一个新的框架,用于识别高可信度联体受体相互作用 (LRIs),用于分析细胞与细胞之间的通信. 它使用机器学习来预测LRIs,并集成单细胞RNA测序数据来进行强大的细胞间通信分析.

关键词:
增强的提升 提升的提升组合学习学习 组合学习细胞间通信是细胞间的通信.连接体受体相互作用

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 细胞与细胞之间的沟通对于生物过程至关重要,主要通过联体受体相互作用 (LRIs) 进行介导.
  • 准确的LRI预测对于理解细胞间通信至关重要,但缺乏用于评估的"黄金标准"数据集.
  • 现有的方法在高可信度的LRI识别和强大的验证方面扎.

研究的目的:

  • 引入CellGiQ,这是一个用于高可信度联体受体相互作用 (LRI) 预测的新框架.
  • 通过将预测的LRIs与单细胞RNA测序 (scRNA-seq) 数据集成来增强细胞间通信分析.
  • 提供一个验证的计算工具,以单细胞分辨率剖析LRI介导的细胞-细胞通信.

主要方法:

  • CellGiQ使用BioTriangle进行LRI特征提取,使用LightGBM进行LRI选择,并通过渐变增强的神经网络和可解释增强机器集成进行分类.
  • 使用scRNA-seq数据过高可信度LRIs,并使用四分位数评分策略应用于细胞间通信推断.
  • 验证涉及AUC/AUPR指标,Venn图,分子对接,贾卡德指数比较和文献检索.

主要成果:

  • 在基于AUC和AUPR的四个数据集上,CellGiQ的表现优于六个竞争中的LRI预测模型.
  • 预测的LRIs通过其他五种细胞间通信推断方法验证,并使用最先进的工具显示出高的Jaccar指数.
  • 推断出与HNSCC相关的细胞间通信结果与经典模型和文献进行了验证.

结论:

  • CellGiQ提供了一种基于机器学习的新方法来识别高可信度LRI,解决了计算LRI预测的关键需求.
  • 该框架提供了强大的验证策略,包括分子对接和与现有方法的比较.
  • CellGiQ是一个开源工具,以单细胞分辨率促进LRI介导的细胞间通信分析.