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Enzyme-linked receptors are cell-surface receptors acting as an enzyme or associating with an enzyme intracellularly. They make excellent drug targets. Drugs can bind to the extracellular ligand-binding domain or directly affect their enzymatic domain and alter their activity.
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Knowledge of anatomy is essential to understand human biology and medicine. Anatomists and health care professionals use standard terminology to describe the human body with more precision and no ambiguity. Anatomical terms have mostly Greek and Latin-derived roots. Because these languages are rarely used in conversation, the meaning of words remains the same. Each term is made up of a root in between the prefixes and suffixes. The root of a term often refers to an organ, tissue, or condition,...
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位置增强的语法知识用于生物医学关系提取.

Yan Zhang1, Zhihao Yang1, Yumeng Yang1

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

Journal of biomedical informatics
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于生物医学关系提取的新型注意力生成器,通过全面使用语法和位置信息来提高准确性,以减少噪音和增强文本表示.

关键词:
生物医学关系提取提取位置信息 位置信息语法知识是语法知识.

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科学领域:

  • 生物医学信息学是生物医学信息学.
  • 自然语言处理自然语言处理.
  • 计算语言学计算语言学

背景情况:

  • 由于复杂的文本,生物医学关系提取具有挑战性.
  • 现有的方法使用语法知识,但缺乏细粒度的降噪.
  • 这可能会导致关系分类中的混.

研究的目的:

  • 为生物医学关系提取提出一个注意力生成器.
  • 综合利用语法依赖类型和位置信息.
  • 通过减少噪声来提高关系分类的准确性.

主要方法:

  • 开发了一个注意力生成器,考虑语法依赖类型和位置.
  • 集成的位置,依赖类型和单词表示.
  • 引入了位置增强的语法知识,用于关系提取.

主要成果:

  • 提出的方法在三个基准数据集上始终优于基线模型.
  • 证明了对语法知识的有效利用.
  • 显著减少了噪音单词的影响.

结论:

  • 新型的注意力生成器有效地增强了生物医学关系提取.
  • 整合不同的语法和位置信息可以提高模型的性能.
  • 该方法在关系分类中为降低噪声提供了强大的解决方案.