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

Longitudinal Research02:20

Longitudinal Research

13.2K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.2K
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

326
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...
326
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

529
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
529
What is Variation?01:14

What is Variation?

18.4K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.4K
Encoding01:19

Encoding

837
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
837
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

553
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...
553

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

A novel SVIR epidemic model with jumps for understanding the dynamics of the spread of dual diseases.

Chaos (Woodbury, N.Y.)·2024
Same author

A New Stochastic Split-Step <i>θ</i>-Nonstandard Finite Difference Method for the Developed SVIR Epidemic Model with Temporary Immunities and General Incidence Rates.

Vaccines·2022
Same author

Estimation of left ventricular parameters based on deep learning method.

Mathematical biosciences and engineering : MBE·2022
Same author

Azathioprine pretreatment ameliorates myocardial ischaemia reperfusion injury in diabetic rats by reducing oxidative stress, apoptosis, and inflammation.

Clinical and experimental pharmacology & physiology·2021
Same author

Effect of indwelling time of double J tube on infected ureteral calculi and the distribution of pathogenic characteristics in diabetics.

American journal of translational research·2021
Same author

Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium.

Royal Society open science·2021
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jan 29, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; 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

1.8K

变化深度联盟:一种生成自动编码方法,用于纵向数据分析.

Shan Feng1, Wenxian Xie1, Yufeng Nie1

  • 1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, China.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
概括
此摘要是机器生成的。

这项研究介绍了变化深度联盟 (VaDA),这是一种用于分析纵向数据的新型深度学习方法. VaDA有效地建模复杂的关系,同时实现预测,集群和表示学习.

关键词:
变化自动编码器 自动编码器聚类集群是指聚类的聚类.深度生成模型深度生成模型纵向数据 纵向数据 纵向数据边际模型是一个边际模型.代表性学习学习学习

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K
Evaluating Skilled Prehension in Mice Using an Auto-Trainer
05:01

Evaluating Skilled Prehension in Mice Using an Auto-Trainer

Published on: September 12, 2019

6.0K

相关实验视频

Last Updated: Jan 29, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; 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

1.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K
Evaluating Skilled Prehension in Mice Using an Auto-Trainer
05:01

Evaluating Skilled Prehension in Mice Using an Auto-Trainer

Published on: September 12, 2019

6.0K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 生物统计学 生物统计学

背景情况:

  • 深度学习对科学研究产生了重大影响,特别是在分析复杂数据集时.
  • 纵向数据对于跟踪随时间的变化至关重要,它带来了独特的分析挑战.
  • 现有的方法往往难以在重复测量中建模复杂的关系.

研究的目的:

  • 引入变化深度联盟 (VaDA),一种用于纵向数据的新型生成深度学习方法.
  • 为了实现同时预测结果,主题聚类和表示学习.
  • 为分析复杂的纵向数据集提供可扩展和强大的框架.

主要方法:

  • 发展变化深度联盟 (VaDA),一种使用变化自动编码器连接重复测量的生成模型.
  • 在一个随机自编码变量贝叶斯框架内实现有效的推理.
  • 适应混合类型变量和可扩展性到大型数据集.

主要成果:

  • 在各种合成场景中,VaDA表现出高度的稳定性和概括能力.
  • 量化比较显示,与基线方法相比,性能优越.
  • 应用到CelebFaces Attributes数据集成功识别了潜在的集群,并生成了高质量的面部图像.

结论:

  • VaDA提供了一个统一的,结构良好的潜伏空间,用于全面的纵向数据分析.
  • 该方法是高效的,可扩展的,强大的,使其适合大规模的科学研究.
  • 对于数据分析和生成任务,如图像合成,VaDA证明是有效的.