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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

615
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
615
Survival Tree01:19

Survival Tree

87
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
87

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

Selection of reference genes and proteins for expression studies in pediatric gliomas.

Discover oncology·2025
Same author

Realistic nitrate concentrations diminish reproductive indicators in <i>Skiffia lermae</i>, an endemic species in critical endangered status.

PeerJ·2024
Same author

Distribution of estrogen receptors alpha and beta in the brain of male rats with same-sex preference.

Physiology & behavior·2023
Same author

Increased oxidative stress contributes to enhance brain amyloidogenesis and blunts energy metabolism in sucrose-fed rat: effect of AMPK activation.

Scientific reports·2021
Same author

Effect of Nicotine on CYP2B1 Expression in a Glioma Animal Model and Analysis of CYP2B6 Expression in Pediatric Gliomas.

International journal of molecular sciences·2018
Same author

Identification of the antiepileptic racetam binding site in the synaptic vesicle protein 2A by molecular dynamics and docking simulations.

Frontiers in cellular neuroscience·2015
Same journal

ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting.

Computers in biology and medicine·2026
Same journal

Electro-osmotic metachronal cilia transport of viscoelastic blood infused with penta-hybrid nanoparticles in an oviduct: Analytical and neural network modeling.

Computers in biology and medicine·2026
Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026
Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026
Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026
Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 6, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

选择,优化和外部验证一个先前存在的机器学习回归算法来估计腰围.

Bryan V Phillips-Farfán1

  • 1Laboratorio de Nutrición Experimental, Instituto Nacional de Pediatría. Insurgentes Sur 3700, Letra "C", Alcaldía Coyoacán, CDMX, 04530, Mexico.

Computers in biology and medicine
|January 5, 2024
PubMed
概括
此摘要是机器生成的。

机器学习使用常见数据,如体重和身高,准确地估计腰围 (WC),克服身体质量指数 (BMI) 的局限性,以更好地预测健康风险.

关键词:
选择算法的算法选择.外部交叉验证的外部验证超参数优化超参数优化机器学习就是机器学习.模型检查检查模型检查回归是一种回归.

更多相关视频

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

相关实验视频

Last Updated: Jul 6, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

科学领域:

  • 医疗信息学 医疗信息学
  • 生物统计学 生物统计学
  • 机器学习 机器学习

背景情况:

  • 体重指数 (BMI) 是一种常见的肥胖度量,但在预测健康风险方面存在局限性.
  • 腰围 (WC) 是肥胖相关疾病风险的更好的预测指标.
  • 现有的数据集通常具有不完整,不准确或缺失的WC数据.

研究的目的:

  • 开发和验证用于准确估计腰围 (WC) 的机器学习模型.
  • 使用易于使用的预测变量 (体重,身高,年龄,性别) 进行WC估计.
  • 提供可靠的方法来评估肥胖,当WC数据是不可用的或不可靠的.

主要方法:

  • 系统的数据清理,包括处理缺失值和异常值.
  • 对现有的回归算法进行交叉验证,以选择表现最佳的模型.
  • 超参数优化和使用各种数据集选择机器学习模型的外部验证.
  • 利用公开可用的数据,包括非成年人,以及常见的预测变量.

主要成果:

  • 调整的机器学习算法使用有限的预测变量准确地估计了WC.
  • 该模型的性能优于以前对WC估计的近似值.
  • 这种方法即使在数据集中的WC测量不完整或不可靠的情况下也是有效的.

结论:

  • 机器学习为估计WC提供了一个强大的解决方案,改进了传统的BMI指标.
  • 这种方法提高了现有数据集对健康风险评估的有用性.
  • 该方法可用于估计其他与健康相关的变量.