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

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

33.9K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
33.9K

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

Corrigendum to: Autoreactive T cell receptors with shared germline-like α chains in type 1 diabetes.

JCI insight·2026
Same author

Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.

Science advances·2026
Same author

Age alters integrated cerebrovascular and cardiovascular dynamic responses to exercise: insights from a systems modeling approach.

Journal of applied physiology (Bethesda, Md. : 1985)·2025
Same author

Smooth and shape-constrained quantile distributed lag models.

Biometrics·2025
Same author

Age Alters Integrated Cerebrovascular and Cardiovascular Dynamic Responses to Exercise: Insights from a Systems Modeling Approach.

medRxiv : the preprint server for health sciences·2025
Same author

Generalized data thinning using sufficient statistics.

Journal of the American Statistical Association·2025
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
查看所有相关文章

相关实验视频

Updated: Jul 14, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

614

对于可解释的细胞类型注释的多解析度分类回归.

Aaron J Molstad1, Keshav Motwani2

  • 1School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA.

Biometrics
|October 5, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种分析多分辨率分类响应的回归模型的新方法. 该方法有效地识别了影响不同类别分辨率的预测因素,为复杂的生物数据提供了洞察力.

关键词:
分类反应回归回归的分类反应回归回归.单元格类型的注释凸优化多项逻辑回归凸优化多项逻辑回归多解决方案学习学习一个单细胞RNA-seqq.

更多相关视频

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.6K
Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

13.8K

相关实验视频

Last Updated: Jul 14, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

614
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.6K
Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

13.8K

科学领域:

  • 统计 统计 统计 统计
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 分类响应回归模型通常具有多分辨率结构,可以将类别分成更粗的层次.
  • 了解预测因素如何影响不同类别分辨率的概率对于准确的建模至关重要.

研究的目的:

  • 提出一种统一的,数据驱动的方法,用于将高维多项逻辑回归模型与多分辨率响应类别相匹配.
  • 为了能够识别与特定类别分辨率相关的预测指标 (粗与细) 或不相关的.

主要方法:

  • 开发了一种用于配合多项逻辑回归模型的新方法,该方法明确解释了响应类别的多分辨率结构.
  • 提出了一个可扩展的算法,适用于重叠和不重叠的粗类定义.
  • 该方法旨在利用固有的多分辨率结构来改善统计属性.

主要成果:

  • 提出的方法成功地根据其对粗细类别的影响来区分预测因素.
  • 统计分析表明,该方法有效地利用了多分辨率结构,优于现有的估计器.
  • 使用基因表达数据对细胞类型注释的应用产生了新的生物学见解.

结论:

  • 开发的方法为分析具有固有的等级结构的复杂分类数据提供了强大的工具.
  • 它提供了一种数据驱动的方法,以在不同级别的类别细分度下确定预测器相关性.
  • 这些发现对改善生物研究中细胞类型注释方法有重大影响.