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

12.4K
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...
12.4K

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

Pain and Nutrition in Dementia and Alzheimer's Phase 1: a cross-sectional, observational study design.

Frontiers in dementia·2026
Same author

Carboxymethyl Cellulose-Stabilized Copper Sulfide Nanoparticles-Based Photothermal Composite Films: Integrating Hydrophobicity, Biodegradability, and Antibacterial Activity for Long-Term Food Preservation.

Biomacromolecules·2026
Same author

Clinical Manifestations.

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

Effect of bronchopulmonary dysplasia and pneumonia on the neurodevelopment of preterm infants: a cerebral blood flow study.

Quantitative imaging in medicine and surgery·2025
Same author

A Bayesian framework for genome-wide circadian rhythmicity biomarker detection.

Briefings in bioinformatics·2025
Same author

Lignin-based microcapsule for pesticide delivery: A review.

International journal of biological macromolecules·2025
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
查看所有相关文章

相关实验视频

Updated: Jun 2, 2025

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
06:40

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

1.2K

以结果为导向的贝叶斯聚类用于使用高维转录组数据发现疾病亚型.

Lingsong Meng1, Zhiguang Huo1

  • 1Department of Biostatistics, University of Florida, Gainesville, FL, USA.

Journal of applied statistics
|January 15, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了指导贝叶斯聚类,这是一种通过整合omics和临床数据来发现具有临床意义的疾病亚型的新方法. 该方法通过识别具有更好的结果预测的患者子组来增强精准医学.

关键词:
贝叶斯的方法 贝叶斯的方法高斯混合模型是高斯的混合模型.以结果为导向的聚类.吉布斯采样 采样

更多相关视频

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.1K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

634

相关实验视频

Last Updated: Jun 2, 2025

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
06:40

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

1.2K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.1K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

634

科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 复杂的疾病表现出显著的异质性,需要精确医学的疾病亚型.
  • 欧米克数据有助于识别疾病亚型,但传统的聚类可能缺乏临床相关性.
  • 临床数据有潜力指导基于omics的子类型,以获得有意义的发现.

研究的目的:

  • 开发一个以结果为导向的贝叶斯聚类方法,整合OMIC和临床数据.
  • 发现具有临床意义的疾病亚型和相关基因.
  • 通过强大的疾病亚型,实现精准医学.

主要方法:

  • 开发了指导贝叶斯集群 (GuidedBayesianClustering),这是一个完全的贝叶斯方法.
  • 利用高斯混合模型进行样本集群和尖石和石先验进行基因选择.
  • 结合了使用混合物模型先验的临床结果变量,并使用吉布斯采样来提高效率.

主要成果:

  • 同时实现疾病亚型的发现,特征的选择和以结果为导向的亚型.
  • 通过模拟和现实世界的数据应用 (乳腺癌,阿尔茨海默病) 证明了卓越的性能.
  • 在GitHub上提供了一个R包,用于更广泛的应用.

结论:

  • 引导贝叶斯聚类有效地整合了omics和临床数据,以发现临床相关的疾病亚型.
  • 这种方法通过提供更有意义的患者分层来推进精准医学.
  • 公开可用的R包有助于采用这种先进的亚型化技术.