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相关概念视频

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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.
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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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相关实验视频

Updated: Jun 30, 2025

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TENET:基于三倍增强的图形神经网络,用于从空间转录组学中重建细胞与细胞相互作用网络.

Yujian Lee1, Yongqi Xu2, Peng Gao3

  • 1Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China; Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region; Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.

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|March 20, 2024
PubMed
概括

我们开发了TENET,一种新的图形神经网络,可以从空间转录组学数据准确地重建细胞与细胞相互作用 (CCI). TENET显著提高了重建的准确性,即使有噪音或稀疏的数据,性能优于现有的方法.

关键词:
细胞与细胞相互作用 网络重建 细胞相互作用 网络重建深度学习是一种深度学习.基因监管网络 基因监管网络图表神经网络的神经网络空间转录学 空间转录学

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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:

  • 细胞通信由信号分子组成的细胞-细胞相互作用 (CCI) 网络进行编排,这对组织功能至关重要.
  • 空间转录学 (ST) 数据可以研究CCI,但浅层神经网络与杂和稀疏的数据作斗争.
  • 现有的从ST数据中进行CCI重建的方法,由于处理数据复杂性的局限性,导致结果不足于最佳.

研究的目的:

  • 提出一种新的方法,TENET (基于三倍增强的图形神经网络),用于从ST数据中准确和全面的CCI重建.
  • 解决浅层神经网络在从杂和稀疏的ST数据中重建CCI方面的局限性.
  • 增强捕获有价值的生物特征,并提高CCI网络推断中的denoising能力.

主要方法:

  • 开发了TENET,一个图形神经网络,包含三个渐进增强机制,用于累积特征提取和降噪.
  • 跨增强阶段的综合知识,以指导CCI网络的解码和重建.
  • 在合成和现实世界的空间转录学数据集上验证了TENET.

主要成果:

  • 与最先进的方法相比,TENET在CCI重建中表现优越.
  • 在平均精度 (AP) 中平均提高了9.61%,在接收器操作特性 (AUROC) 下面面积 (AUROC) 中平均提高了7.32%.
  • 该方法有效地处理空间转录学数据中固有的稀疏连接和噪声.

结论:

  • TENET提供了一种强大而准确的方法,用于从空间转录学数据中重建细胞-细胞相互作用网络.
  • 拟议的三倍增强机制显著提高了捕获生物信号和减轻噪声的能力.
  • 在分析组织中细胞通信的计算方法中,TENET代表了实质性的进步.