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

Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

604
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
604
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

378
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
378
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
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...
53
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
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...
69
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

491
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...
491

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

Activation of the endocytosis pathway stratifies subtypes and therapeutic sensitivity in colorectal cancer.

Research square·2026
Same author

Air pollution is linked to divergent cortical thickness patterns in brain regions vulnerable to Alzheimer's disease.

Neurotoxicology·2026
Same author

Association between late-life air pollution exposure and medial temporal lobe atrophy in older women.

Neurotoxicology·2026
Same author

Clinical and molecular characterization of <i>AXL</i> in colorectal cancer, CALGB (Alliance)/SWOG 80405 and real-world data.

Journal for immunotherapy of cancer·2025
Same author

DNA methylation: a potential mediator between air pollution exposures and asthma control.

Clinical epigenetics·2025
Same author

GABAergic signaling contributes to tumor cell invasion and poor overall survival in colorectal cancer.

Oncogene·2025

相关实验视频

Updated: Jun 30, 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

CausNet-partial:基于"部分代序"的搜索,通过使用父集合约束的动态编程来寻找最佳的稀疏贝叶斯网络.

Nand Sharma1, Joshua Millstein1

  • 1Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, USA.

Research square
|March 18, 2024
PubMed
概括

我们介绍CausNet-partial,这是一个有效的算法,用于发现最佳稀疏贝叶斯网络 (BNs). 这种方法显著减少了搜索空间和运行时间,使得对复杂数据类型的成千上万个变量进行分析.

关键词:
最佳贝叶斯网络的最佳贝叶斯网络动态编程是动态的编程.一代一代的订单.

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
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

相关实验视频

Last Updated: Jun 30, 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

1.1K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

科学领域:

  • 计算生物学 计算生物学
  • 机器学习 机器学习
  • 统计建模 统计建模

背景情况:

  • 开发了CausNet,这是一个动态编程算法,用于具有父集合约束的最佳贝叶斯网络 (BNs).
  • CausNet有效地搜索BN空间以获得各种结果的连续和分类数据.
  • 引入了CausNet-partial,一种优化搜索的变体,对稀疏的BN进行部分代序排序.

研究的目的:

  • 开发和评估CausNet-partial,以有效地发现较小,稀疏的最佳贝叶斯网络.
  • 为了证明算法的可扩展性,以数千个变量的数据集.
  • 将CausNet-partial的性能与现有的最先进的算法进行比较.

主要方法:

  • 实现了一个基于部分代序的动态编程搜索算法.
  • 在合成连续数据和ALARM基准离散贝叶斯网络上测试了CausNet和CausNet-partial.
  • 在CausNet-partial中改变部分订单参数,以评估对网络发现和运行时间的影响.

主要成果:

  • CausNet-partial显著减少了寻找空间和运行时间,以寻找小型,稀疏的最佳BNs.
  • 该算法的性能优于广泛使用的三种最先进的BN发现算法.
  • 对模拟数据和ALARM网络的成功应用,更快地发现较小的网络.

结论:

  • CausNet-partial提供了一种高效且可扩展的方法来识别最佳的稀疏贝叶斯网络.
  • 该算法支持各种数据类型 (连续,分类,生存) 和评分选项 (BIC,Bge).
  • 可调节的参数允许控制网络大小和密度,使其适用于高维数据分析.