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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.4K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.4K
Survival Tree01:19

Survival Tree

84
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...
84

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

Data Resource Profile: Cheeloo Lifespan Electronic-health reseArch Data-library (Cheeloo LEAD).

International journal of epidemiology·2026
Same author

Integrative omics analysis incorporating cardiovascular magnetic resonance imaging pinpoints potentially druggable plasma proteins for cardiovascular diseases.

Life metabolism·2026
Same author

Multivariate genetic analysis reveals three distinct pathological dimensions in musculoskeletal disorders.

Nature communications·2026
Same author

Transfer learning-based two-sample Mendelian randomization method for heterogeneous population.

Briefings in bioinformatics·2026
Same author

SVAtlas: a comprehensive single extracellular vesicle omics resource.

Nucleic acids research·2025
Same author

Small-Area Lung Cancer Incidence and Mortality: Cross-Sectional Population-Based Study Using Hospital Discharge and Death Registration Data.

JMIR public health and surveillance·2025
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 30, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K

MRSL:基于GWAS总结数据的因果网络修剪算法.

Lei Hou1, Zhi Geng2, Zhongshang Yuan3,4

  • 1Beijing International Center for Mathematical Research, Peking University, Beijing, People's Republic of China, 100871.

Briefings in bioinformatics
|March 15, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了MRSL,一种使用遗传数据进行因果发现的新算法. MRSL有效地从观测数据中发现复杂的因果网络,改进了现有的方法.

关键词:
因果发现的发现.食管状细胞癌的癌症图形理论中的图形理论.门德尔的随机化是门德尔的随机化网络修剪 剪裁 网络修剪血清代谢物的血清代谢物

更多相关视频

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.3K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

相关实验视频

Last Updated: Jun 30, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.3K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

科学领域:

  • 遗传学 是一个遗传学.
  • 因果推理因果推理
  • 生物信息学是一种生物信息学.

背景情况:

  • 观察数据分析对于理解生物系统至关重要.
  • 遗传变异为因果结构学习提供了互补的见解.
  • 门德尔随机化 (MR) 研究已经确定了许多边缘因果关系.

研究的目的:

  • 开发一种新的因果网络修剪算法,MRSL (基于MR的结构学习算法).
  • 为了利用MR研究中的边际因果关系来增强结构学习.
  • 仅使用全基因组关联研究 (GWAS) 总结统计数据推断条件因果结构.

主要方法:

  • MRSL将图形理论与多变量MR集成在一起.
  • 该算法使用拓分类来提高结构学习精度.
  • MRSL引入了MR分离和候选分离集,取代了传统的d分离.

主要成果:

  • 模拟显示,MRSL的F1得分高达2倍,比竞争方法快100倍.
  • 应用到英国生物银行GWAS数据的26个生物标志物和44种疾病的应用确定了预期和新的因果关系.
  • 已识别的链接具有生物解释,并由现有的文献或临床报告支持.

结论:

  • MRSL是一种高效和精确的算法,用于从GWAS总结统计数据中发现因果关系.
  • 该方法有效地确定了特征和疾病之间的生物学相关因果关系.
  • 通过整合遗传数据和先进的图形算法,MRSL推进了因果推理领域.