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

3.8K
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...
3.8K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
64
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

83
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
83
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

618
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
618
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

50
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
50
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

51
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
51

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

Multiplex networks-based directed graph neural network for cancer driver gene identification.

PLoS computational biology·2026
Same author

LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.

Interdisciplinary sciences, computational life sciences·2023
Same author

Drug response prediction using graph representation learning and Laplacian feature selection.

BMC bioinformatics·2022
Same journal

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026
Same journal

ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Journal of bioinformatics and computational biology·2026
Same journal

When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis.

Journal of bioinformatics and computational biology·2026
Same journal

Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction.

Journal of bioinformatics and computational biology·2026
Same journal

Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks.

Journal of bioinformatics and computational biology·2026
Same journal

Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.

Journal of bioinformatics and computational biology·2026
查看所有相关文章

相关实验视频

Updated: Jun 13, 2025

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

18.5K

基于半隐性图形变化自动编码器的多药副作用预测.

Zhou Yi1, Minzhu Xie1,2

  • 1College of Information Science and Engineering, Hunan Normal University, Changsha 410081, P. R. China.

Journal of bioinformatics and computational biology
|September 12, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了SIPSE,一种新的计算方法,用于预测组合药物的副作用. 通过建模药物特征和使用图形神经网络来更好地预测关联,SIPSE提高了准确性.

关键词:
多种药物的副作用 多种药物的副作用链接预测 链接预测半隐性图形变化自动编码器

更多相关视频

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

983
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

1.6K

相关实验视频

Last Updated: Jun 13, 2025

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

18.5K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

983
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

1.6K

科学领域:

  • 计算机化药物发现.
  • 药物基因组学 药物基因组学
  • 生物信息学是一种生物信息学.

背景情况:

  • 多药性对复杂疾病至关重要,但会增加副作用的风险.
  • 由于确定性嵌入,现有的计算方法难以捕捉微妙的药物关联.
  • 准确预测多药副作用对于患者安全至关重要.

研究的目的:

  • 开发一种新的计算方法,SIPSE,用于预测多药副作用.
  • 改进潜伏药物空间的建模,以便更好地预测关联.
  • 整合多样化的数据源,以增强药物特征表示.

主要方法:

  • SIPSE利用单一药物的副作用数据和药物向蛋白相互作用.
  • 一个半隐含的图形变化自动编码器模拟了多药副作用,并产生了灵活的潜分布.
  • 通过噪声嵌入和邻里共享来传播不确定性,可以增强图形分析.

主要成果:

  • SIPSE通过从学习分布中采样节点嵌入来有效地预测多药学副作用.
  • 该方法在基准数据集上与五种最先进的方法相比显示出更高的性能.
  • 药物特征的整合和基于图形的建模在捕捉复杂的关联方面被证明是有效的.

结论:

  • SIPSE在预测多药副作用方面取得了重大进展.
  • 该方法为了解药物相互作用和潜在不良事件提供了更强大的方法.
  • 这项工作为更安全,更有效的多药疗法铺平了道路.