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

Introduction to z Scores01:05

Introduction to z Scores

673
A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
673
Multiple Regression01:25

Multiple Regression

3.2K
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...
3.2K
Stratified Sampling Method01:16

Stratified Sampling Method

12.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.8K
z Scores and Unusual Values01:07

z Scores and Unusual Values

10.1K
The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
10.1K
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

7.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
7.3K

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

Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.

Neuron·2026
Same author

Spatio-molecular gene expression reflects dorsal anterior cingulate cortex structure and function in the human brain.

Cell reports·2026
Same author

Distinct cellular DNA methylation mechanisms underlie common and rare genetic risk for brain disorders.

bioRxiv : the preprint server for biology·2026
Same author

Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex.

bioRxiv : the preprint server for biology·2026
Same author

Plasma Proteomic Analysis of <i>APOE</i> ε4 Homozygotes Identifies Preclinical Alzheimer's Disease Alterations Potentially Treatable with Semaglutide.

medRxiv : the preprint server for health sciences·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

Multi-modal mixed-type structural equation modeling with structured sparsity for subgroup discovery from heterogeneous health data.

IISE transactions·2025
Same journal

Discriminant Subgraph Learning from Functional Brain Sensory Data.

IISE transactions·2023
Same journal

A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data.

IISE transactions·2023
Same journal

Access Planning and Resource Coordination for Clinical Research Operations.

IISE transactions·2020
查看所有相关文章

相关实验视频

Updated: Sep 10, 2025

Author Spotlight: IntelliSleepScorer &#8212; A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

651

没有标记数据的潜在结构预测分数的排名和组合

Shiva Afshar1, Yinghan Chen2, Shizhong Han3

  • 1Department of Neurology, Emory University, Atlanta, GA, 30322, USA.

IISE transactions
|August 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的结构化无监督集体学习 (SUEL) 模型,有效地结合多个预测因素,而无需标记数据. SUEL模型对依赖性预测因素进行排名和整合,从而提高各种应用中的预测准确性.

关键词:
进行分类相关预测分数发现风险基因无监督组合学习

更多相关视频

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

相关实验视频

Last Updated: Sep 10, 2025

Author Spotlight: IntelliSleepScorer &#8212; A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

651
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

科学领域:

  • 机器学习
  • 生物信息学
  • 数据科学

背景情况:

  • 从分布式数据源中结合预测因素可以提高预测的准确性.
  • 评估预测器的准确性通常需要大量的标记数据,而这些数据往往很难获得.
  • 在集体学习中常见的相关预测因素带来了整合挑战.

研究的目的:

  • 开发一种新的结构化无监督集体学习 (SUEL) 模型,用于整合没有标记数据的预测器.
  • 解决未知预测器准确性和高预测器相关性的挑战.
  • 有效地排列和结合预测因素以提高超级学习者的表现.

主要方法:

  • 引入了一种新的结构化无监督集体学习 (SUEL) 模式.
  • 开发了两种基于关联的分解算法:受约束的二次优化 (SUEL.CQO) 和基于矩阵的分解算法 (SUEL.MF).
  • 通过模拟研究和真实世界的风险基因发现应用来评估SUEL模型.

主要成果:

  • 在不需要地面真相数据的情况下,SUEL模型成功地对预测器进行排名.
  • 提出的SUEL.CQO和SUEL.MF方法可以有效估计SUEL模型.
  • 整体模型有效地整合了依赖预测器,证明了增强的性能.

结论:

  • 建议的SUEL方法为整合无标记数据的相关预测因素提供了有效的解决方案.
  • 这种方法提高了超学习者在有限的基础真理的预测问题上的表现.
  • 这些方法对生物信息学中的风险基因发现等应用具有前景.