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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

226
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...
226
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.3K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.3K
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

1.1K
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...
1.1K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

875
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
875
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

223
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...
223

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 Practical Identifiability Criterion Leveraging Weak-Form Parameter Estimation.

Bulletin of mathematical biology·2026
Same author

Synthesizing selection mosaic theory and host-pathogen theory to explain large-scale pathogen coexistence.

Nature communications·2025
Same author

WEAK FORM LEARNING FOR MEAN-FIELD PARTIAL DIFFERENTIAL EQUATIONS: AN APPLICATION TO INSECT MOVEMENT.

ArXiv·2025
Same author

Learning Structured Population Models from Data with WSINDy.

ArXiv·2025
Same author

Dynamic, behavior-dependent interactions between dorsal striatal dopamine and glutamate release predict cognitive flexibility and punishment resistant cocaine use.

bioRxiv : the preprint server for biology·2025
Same author

A practical identifiability criterion leveraging weak-form parameter estimation.

ArXiv·2025
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
查看所有相关文章

相关实验视频

Updated: Jan 9, 2026

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.8K

使用WSINDy从数据中学习结构化人口模型.

Rainey Lyons1, Vanja Dukic1, David M Bortz1

  • 1Department of Applied Mathematics, University of Colorado, Boulder, Colorado, United States of America.

PLoS computational biology
|December 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的科学机器学习方法,以有效地识别从数据中驱动人口变化的关键因素. 该方法简化了复杂人口动态的建模,包括异质人口.

更多相关视频

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

2.6K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.7K

相关实验视频

Last Updated: Jan 9, 2026

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.8K
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

2.6K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.7K

科学领域:

  • 人口动态 人口动态
  • 科学机器学习科学机器学习
  • 计算生物学 计算生物学

背景情况:

  • 人口动态受到个体特征的影响,如年龄和大小.
  • 在人口模型中识别关键因素 (例如,生育率,死亡率) 在计算上具有挑战性,特别是在异质人口中.
  • 现有的方法与杂的数据和学习复杂的动态斗争.

研究的目的:

  • 开发一种微弱形式的科学机器学习 (WSINDy) 方法,用于为结构化群体选择模型组件.
  • 扩展WSINDy,直接从杂的时间序列直方图数据学习异质人口动态和边界过程.
  • 将学习边界过程的微调超参数纳入交叉验证.

主要方法:

  • 建议扩展非线性动力学 (WSINDy) 弱形式稀疏识别的方法.
  • 将该方法应用于杂的时间序列组图数据,以识别模型成分.
  • 包含了用于超参数调整的交叉验证技术.

主要成果:

  • 成功地证明了该方法在标准年龄和规模结构化人口模型上的性能.
  • 展示了从数据中学习异质动态和边界过程 (例如出生) 的能力.
  • 检查了优点和局限性,专注于图书馆术语的区分能力.

结论:

  • 拟议的WSINDy扩展为人口动态中的模型选择提供了一种高效的方法.
  • 该方法有效处理杂的数据,并学习复杂的,异构的人口动态.
  • 这种方法提升了通过直接推断模型组件和边界过程来建模结构化群体的能力.