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相关概念视频

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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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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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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基于自动编码多关系图的多任务学习来预测协同药物组合.

Wenyu Shan1, Cong Shen2, Lingyun Luo1,3

  • 1School of Computer Science, University of South China, Hengyang, Hunan 421001, China.

iScience
|October 19, 2023
PubMed
概括
此摘要是机器生成的。

预测复杂疾病的协同药物组合是困难的. 一个新的计算框架VGAETF有效地使用多关系图识别了强有力的药物配对,从而推进了组合药物治疗.

关键词:
卫生科学 卫生科学卫生技术 卫生技术 卫生技术医学 医学 医学 医学 医学药理学 药理学是指药理学的学科.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 药理学 药理学是指药理学的学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 组合药物治疗提供了一个有希望的策略来治疗复杂的疾病,利用协同作用的药物效应.
  • 预测有效的药物组合仍然是一个重大挑战,因为复杂的生物系统和疾病机制和药物点的不完全知识.

研究的目的:

  • 提出一个计算框架,VGAETF (变量图自编码器张量分解),用于预测与疾病相关的协同药物组合.
  • 利用多关系图来建模复杂的生物实体相互作用.

主要方法:

  • 开发了VGAETF,这是一个使用多关系图的端到端计算框架.
  • 采用变量图自编码器和张量分解技术来建模复杂的关系.
  • 基于已知的生物相互作用预测的协同药物组合.

主要成果:

  • VGAETF表现出高预测性能,接收器操作特征曲线下的面积 (AUROC) 为0.9767和精度召回曲线下的面积 (AUPR) 为0.9660.
  • 拟议的框架在预测协同药物组合方面优于现有方法.
  • 案例研究验证了VGAETF在识别潜在的与疾病相关的协同药物对中的有效性.

结论:

  • VGAETF提供了一种有效的计算方法,用于预测协同作用的药物组合.
  • 该框架推进了基于图形的深度学习在药物发现和组合疗法中的应用.
  • VGAETF有可能加速对复杂疾病的新型组合疗法的开发.