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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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前CCI:可解释的深度学习框架,用于识别单细胞转录组中特定细胞类型之间的关键体受体相互作用.

Hanbyeol Kim1, Eunyoung Choi1, Yujeong Shim1

  • 1Bioinformatics Branch, National Cancer Center, Goyang 10408, Republic of Korea.

International journal of molecular sciences
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概括
此摘要是机器生成的。

作为一个深度学习框架,PriorCCI增强了单细胞RNA测序数据中细胞与细胞相互作用 (CCI) 的分析. 它准确地识别了复杂瘤微环境中的关键连接体-受体相互作用.

关键词:
血管新生是因为血管新生.细胞与细胞的相互作用.卷积神经网络是一种卷积神经网络.细胞内皮细胞的内皮细胞.可以解释的人工智能AI确定优先级,确定优先级.单细胞RNA-seq数据的数据瘤微环境是一个微环境.

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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:

  • 了解细胞与细胞相互作用 (CCI) 对于研究免疫反应和癌症等生物过程至关重要.
  • 目前使用单细胞RNA测序 (scRNA-seq) 数据的方法与稀疏性和异质性作斗争,经常缺少关键相互作用.
  • 现有的统计和基于网络的技术缺乏精确度来优先考虑具有生物学意义的CCI.

研究的目的:

  • 开发一个深度学习框架,PriorCCI,用于从scRNA-seq数据中进行可扩展,可解释和生物学意义上的CCI识别.
  • 克服现有方法的局限性,以优先考虑复杂生物系统中的联体受体相互作用.
  • 提供一种强大的方法来分析跨细胞类型的基因相互作用,特别是在瘤微环境中.

主要方法:

  • 开发了PriorCCI,这是一个使用卷积神经网络 (CNN) 的深度学习框架.
  • 集成的Grad-CAM++,一个可解释的人工智能算法,用于对基因对贡献的视觉解释.
  • 将框架应用于来自复杂环境的单细胞RNA测序数据,例如瘤.

主要成果:

  • PriorCCI有效地优先考虑瘤微环境中的癌细胞和其他细胞类型之间的相互作用.
  • 该框架准确地识别了生物学上重要的相互作用,包括与血管生成相关的相互作用.
  • 对基因对贡献的视觉解释增强了推断的基因基因相互作用的稳定性.

结论:

  • PriorCCI提供了一种强大且可解释的方法,用于从scRNA-seq数据系统地识别和优先考虑CCI.
  • 该框架解决了复杂生物样本数据稀疏性和异质性所带来的挑战.
  • 在生理和病理的背景下,PriorCCI促进了对细胞通信网络的更深入的洞察.