Jove
Visualize
联系我们

相关概念视频

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

90
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
90
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

57
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
57
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

45
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
45
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

74
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...
74
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

733
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...
733
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

79
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.
79

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

Using smartphone surveys to predict next-week suicide attempts.

Journal of psychopathology and clinical science·2026
Same author

Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs.

Statistical methods in medical research·2026
Same author

Brainstem Correlates of Tinnitus and Hyperacusis in Normal-Hearing Listeners: Distinct Neural Signatures Linked to Cochlear Nerve Degeneration.

Ear and hearing·2026
Same author

Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment.

IEEE transactions on affective computing·2026
Same author

Associations between self-reported personal care products use and menstrual cycle length and regularity in a US digital cohort.

Environment international·2026
Same author

A village health worker intervention to reduce cardiovascular disease risk in remote areas of armed conflict in Myanmar-results from a feasibility study in three villages.

Conflict and health·2026
Same journal

Flexible Bayesian inference on partially observed epidemics.

Journal of complex networks·2024
Same journal

The distance backbone of complex networks.

Journal of complex networks·2024
Same journal

Modelling the impact of social distancing and targeted vaccination on the spread of COVID-19 through a real city-scale contact network.

Journal of complex networks·2022
Same journal

Graph-based feature extraction and classification of wet and dry cough signals: a machine learning approach.

Journal of complex networks·2022
Same journal

The role of age in the spreading of COVID-19 across a social network in Bucharest.

Journal of complex networks·2021
Same journal

The impact of human mobility networks on the global spread of COVID-19.

Journal of complex networks·2021
查看所有相关文章
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

相关实验视频

Updated: Jul 12, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

将机械网络模型转换为概率模型的框架.

Ravi Goyal1, Victor De Gruttola2, Jukka-Pekka Onnela3

  • 1Division of Infectious Diseases and Global Public, Health, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA.

Journal of complex networks
|October 24, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了将机械网络模型 (MNM) 转换为概率网络模型 (PNM) 的框架. 这允许更好地比较和分析由不同模型生成的网络属性.

关键词:
机械模型是机械模型.网络 网络 网络 网络 网络 网络概率模型是一种概率模型.

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.2K

相关实验视频

Last Updated: Jul 12, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.2K

科学领域:

  • 网络科学 网络科学
  • 计算建模计算建模
  • 系统生物学 系统生物学

背景情况:

  • 存在两个主要的网络建模方法:机械和概率.
  • 机械模型捕捉了潜在的过程,但很难推断.
  • 概率模型有助于推断,但可能不能完全代表生成机制.

研究的目的:

  • 为将机械网络模型 (MNM) 转换为概率网络模型 (PNM) 制定一个一般框架.
  • 为了使不同机械模型产生的网络属性的定量比较.
  • 弥合机械和概率网络建模范式之间的差距.

主要方法:

  • 为MNM转换为PNM引入一个新的框架.
  • 识别基本的网络属性及其共同的概率分布.
  • 应用框架来分析网络属性分布.

主要成果:

  • 该框架允许识别关键的网络属性及其从MNM的概率分布.
  • 允许直接比较不同机械模型的网络输出.
  • 与参考模型对财产表示 (例如,聚类) 的评估.

结论:

  • 拟议的框架成功地将机械和概率网络建模相结合.
  • 通过概率表示来增强机械模型的分析能力.
  • 突出了概率网络模型未来发展的领域.