Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

3.6K
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.
Such synergistic combinations...
3.6K
Effects of Chemicals: Overview01:27

Effects of Chemicals: Overview

1.2K
Drugs, encompassing various chemical compounds from natural sources, lab synthesis, or genetic engineering, elicit different biological responses in living organisms. Some of these responses are desirable or therapeutic, while others are undesirable. The primary goal of administering a drug is to achieve a therapeutic effect, that is, to address a specific disease or health condition. Any concurrent effects outside of this therapeutic outcome are considered undesirable. These undesirable...
1.2K
Pharmacovigilance01:19

Pharmacovigilance

732
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
732
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

8.1K
The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
8.1K
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

254
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
254
Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

1.6K
When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
1.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Conformational hijacking of lipoprotein transporter LolDF enables precision antimicrobial activity against Acinetobacter baumannii.

Nature communications·2026
Same author

A Review of Cybersecurity Issues in Smart Meter-Based Energy Trading.

Sensors (Basel, Switzerland)·2026
Same author

Multi-granularity transformer contrastive learning and feature reconstruction for prediction of disease-related miRNAs.

BMC bioinformatics·2026
Same author

The Impact of Nursing Education Innovation on the Quality of Care for Elderly Hospitalized Patients: A Systematic Review Based on Student Competency Development.

Journal of multidisciplinary healthcare·2026
Same author

Precise estimation of tissue microstructure with hybrid graph transformer.

Artificial intelligence in medicine·2026
Same author

Unprecedented Meroterpenoids Exert Anti-inflammatory Activity by Targeting NF-κB and PI3K Signaling Pathways.

Organic letters·2026

相关实验视频

Updated: May 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.5K

多知识图和多视图实体特征学习用于预测与药物相关的副作用.

Ping Xuan1,2, Tianhong Cheng1, Hui Cui3

  • 1School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.

Journal of chemical information and modeling
|May 6, 2025
PubMed
概括

这项研究引入了MVDSA,这是通过整合多个知识图来预测药物副作用的新模型. 通过准确识别潜在的不良事件,MVDSA提高了药物安全性,并加速了药物开发.

更多相关视频

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.3K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.1K

相关实验视频

Last Updated: May 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.5K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.3K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.1K

科学领域:

  • 药理学和生物信息学 药理学和生物信息学
  • 计算机化药物发现技术
  • 医疗保健中的人工智能

背景情况:

  • 预测药物的副作用对于患者安全和有效的药物开发至关重要.
  • 现有的基于图形的方法往往无法充分利用各种知识图形拓和语义的全部潜力.
  • 需要先进的计算模型,可以整合多视图信息,以准确预测药物副作用关联.

研究的目的:

  • 提出MVDSA,一种用于药物副作用关联预测的新型多视图模型.
  • 为了有效地整合多个关系语义,局部图表拓和多视图实体对特征.
  • 提高计算药物副作用预测的准确性和可靠性.

主要方法:

  • 基于药物/副作用相似性和已知的关联,构建两个知识图.
  • 开发一个空间敏感的学习策略,使用关系式语义编码器来进行自适应性特征学习.
  • 实现连接敏感尾部实体注意力机制和知识图层级注意力机制,用于特征融合.
  • 使用多视图增强的多层感知器 (MLP) 进行最终的关联预测.

主要成果:

  • 在药物副作用关联预测方面,MVDSA显著超过了10种最先进的方法.
  • 废弃性研究证实了拟议组件的有效性,包括注意力机制和多视角学习.
  • 案例研究表明,MVDSA能够识别特定药物的潜在不良药物反应.

结论:

  • 通过整合多样化的知识图信息,MVDSA提供了一种强大而有效的方法来预测药物副作用的关联.
  • 该模型能够捕捉复杂的关系和多视图功能,提高了其预测能力.
  • MVDSA对推进计算药物安全性评估和帮助制药研究具有前途.