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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

92
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
92
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

381
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
381
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

37
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
37
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

39
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...
39
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
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...
56
Econometric Views (EViews)01:29

Econometric Views (EViews)

111
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
111

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

Coexistence of balance and hierarchies: An ego perspective to explain empirical networks.

PNAS nexus·2025
Same author

The nonlinear economy: How resource constraints lead to business cycles.

Chaos (Woodbury, N.Y.)·2025
Same author

Adapting to disruptions: Managing supply chain resilience through product rerouting.

Science advances·2024
Same author

Predicting variable-length paths in networked systems using multi-order generative models.

Applied network science·2023
Same author

The Downside of Heterogeneity: How Established Relations Counteract Systemic Adaptivity in Tasks Assignments.

Entropy (Basel, Switzerland)·2021

相关实验视频

Updated: Jun 2, 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

2.1K

经验网络稀疏:增强零通胀的多边缘模型.

Giona Casiraghi1, Georges Andres1

  • 1Chair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, Zurich 8092, Switzerland.

PNAS nexus
|January 16, 2025
PubMed
概括

现实世界的网络是稀疏的,大多数节点对是断开的. 将零通胀整合到网络模型中准确地捕捉了这种稀疏性,改善了复杂系统的表示.

科学领域:

  • 网络科学 网络科学
  • 数据科学是数据科学.
  • 统计建模 统计建模

背景情况:

  • 经验网络表现出显著的稀疏性,这意味着大多数潜在的连接都不存在.
  • 传统的网络模型很难在现实数据中表示大量断开连接的节点对.
  • 像配置和随机区块模型这样的现有模型无法准确地捕捉网络稀疏性.

研究的目的:

  • 解决传统网络模型在表示网络稀疏性方面的局限性.
  • 引入和评估用于分析现实世界网络的零膨胀模型.
  • 通过计算多余的零 (脱节对) 来提高网络模型的准确性.

主要方法:

  • 分析来自Sociopatterns存储库的所有数据集.
  • 零膨胀网络模型与经典模型的实施和比较.
  • 在捕捉稀疏性和重尾边缘分布方面评估模型性能.

主要成果:

  • 零膨胀模型提供了更准确的真实世界网络稀疏性的表示.
  • 这些模型有效地捕捉了经验数据中观察到的边缘计数的重尾分布.
  • 经典网络模型表现出显著的偏差,因为它们无法解释过多的零.
关键词:
复杂的网络复杂的网络.多边缘的多边缘稀缺性是一种稀缺性.统计建模 统计建模在零通货膨胀的情况下.

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

987
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

相关实验视频

Last Updated: Jun 2, 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

2.1K
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

987
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

结论:

  • 零通胀对于准确建模稀疏的现实世界网络至关重要.
  • 没有纳入零通货膨胀导致有偏见的网络模型和不准确的系统动态.
  • 零膨胀模型为理解复杂系统及其相互作用提供了卓越的框架.