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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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DiffMLP:在知识图中基于扩散的多跳环预测框架.

Hao Liu1, Dong Li1, Bing Zeng1

  • 1School of Software Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.

Neural networks : the official journal of the International Neural Network Society
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PubMed
概括
此摘要是机器生成的。

本研究介绍了DiffMLP,这是一个用于知识图中的多跳链接预测的新框架. DiffMLP通过扩散过程来建模行为来增强推理,实现最先进的结果.

关键词:
有条件的无雾化器扩散过程中的扩散过程.完成知识图表的完成.多跳链接预测多跳链接预测

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科学领域:

  • 人工智能的人工智能
  • 数据科学数据科学数据科学
  • 图形数据库 图形数据库

背景情况:

  • 在知识图中预测多跳链接是复杂的,因为复杂的推理路径和不确定性.
  • 目前的方法很难在推理上下文和邻里信息之间的依赖关系上建模.

研究的目的:

  • 引入DiffMLP,这是一个用于多节点链接预测的新框架.
  • 解决知识图推理中的交互依赖性建模方面的局限性.

主要方法:

  • DiffMLP将每个跳跃的作用空间模拟为条件分布,使用反向扩散过程.
  • 一个基于注意力的图形,以推理上下文为指导,改进了行动空间嵌入.
  • 规范化的噪声注入和先前的约束稳定和调节扩散过程.

主要成果:

  • 在四个基准数据集上,DiffMLP实现了最先进的性能.
  • 在FB15K-237上,DiffMLP比之前的最佳模型提高了7.0%的平均互惠等级 (MRR) 和12.7%的Hits@3.

结论:

  • DiffMLP在知识图的多跳链接预测方面取得了重大进展.
  • 基于扩散的方法有效地捕捉了复杂的推理依赖性和不确定性.