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

Genetic Screens02:46

Genetic Screens

4.6K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
4.6K
P-value01:10

P-value

7.1K
P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
7.1K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.8K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

2.9K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
2.9K
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.4K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.4K

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

Incision and plasty of the descending duodenal diverticulum using an ESD technique in a child with acute pancreatitis: a case report.

BMC pediatrics·2026
Same author

Excited-State Intramolecular Proton Transfer of Deprotonated Fisetin in Poly(vinyl alcohol) Films.

The journal of physical chemistry. B·2026
Same author

Broadly tunable continuous-wave Tm:CALYO laser operating on the <sup>3</sup>H<sub>4</sub>→<sup>3</sup>H<sub>5</sub> transition.

Optics express·2026
Same author

DIFC-Net: Diffusion-Intrinsic Feature Capture for AI-Generated Image Detection.

Sensors (Basel, Switzerland)·2026
Same author

A Modified Normalized Power Prior Approach for Bayesian Adaptive Borrowing in Item Response Theory Models.

Statistics in medicine·2026
Same author

Circular RNA hsa_circ_0003472 Promotes Pancreatic Ductal Adenocarcinoma Progression and Gemcitabine Resistance Via the mir-1253/ERCC1 Axis.

Biological procedures online·2026

相关实验视频

Updated: May 2, 2026

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Published on: March 3, 2015

13.2K

PDC-MAKES:一种条件选方法,用于控制高维多响应设置中的错误发现.

Wei Xiong1, Han Pan2,3, Tong Shen1

  • 1School of Statistics, University of International Business and Economics, Beijing, 100029, China.

Biometrics
|April 25, 2025
PubMed
概括

这项研究引入了一种新的无模型方法,用于识别复杂数据集中的重要预测因素. 它有效地控制了错误发现率 (FDR),即使使用高维和相关数据.

关键词:
有条件的查.错误发现率控制 错误发现率控制多变量反应的多变量反应.部分距离相关性 部分距离相关性

更多相关视频

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

7.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K

相关实验视频

Last Updated: May 2, 2026

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Published on: March 3, 2015

13.2K
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

7.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 数据中的高维度和强烈的相关性对识别关键预测因素构成挑战.
  • 现有的方法在多响应设置和复杂的数据结构方面扎.

研究的目的:

  • 为超高维,多响应数据提出一种无模型的条件特征选方法.
  • 控制错误发现率 (FDR),同时识别重要的预测因素.

主要方法:

  • 使用部分距离相关性来测量随机向量之间的依赖性,控制多变量效应.
  • 采用随机化的仿制值来稳定值和错误发现率控制.
  • 开发一个强大的选方法,用于重尾数据和相关预测器.

主要成果:

  • 该方法识别了与反应略有无关但有条件相关的预测因素.
  • 实现可靠的选属性,保持FDR控制,并提供更高的统计能力.
  • 对重尾分布和高预测器相关性表现出强度.

结论:

  • 拟议的方法为超高维设置中的特征选提供了一种强大而稳健的方法.
  • 它通过允许对高维变量进行条件化来推进当前的研究.
  • 该方法在模拟和现实数据应用中显示出卓越的性能.