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

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

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same author

Multivariate Random Forests for Cross-Modal Multi-Omics Integration.

bioRxiv : the preprint server for biology·2026
Same author

A data-driven modeling framework for mapping genotypes to synthetic microbial community functions.

Cell systems·2026
Same author

Longitudinal Blood DNA Methylation Changes During Weight-Loss Intervention and Dementia Progression Risk.

Research square·2026
Same author

Tuning intermetallic growth and flexural performance in gallium-based amalgams via Sn/In alloying and solidifying at controlled temperature.

Scientific reports·2026
Same author

From aging to Alzheimer's disease: concordant brain DNA methylation changes in late life.

Genome medicine·2026

相关实验视频

Updated: May 22, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.1K

一个整合性的多omics随机森林框架,用于稳健的生物标志物发现.

Wei Zhang1, Hanchen Huang1, Lily Wang1,2,3,4

  • 1Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA.

bioRxiv : the preprint server for biology
|March 17, 2025
PubMed
概括

这项研究引入了一种新的方法,用于在多个omics数据类型中找到关键生物标志物. 具有逆最小深度 (IMD) 的多变量随机森林 (MRF) 框架有效地识别了疾病研究的重要生物标记.

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
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

933

相关实验视频

Last Updated: May 22, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.1K
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
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

933

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:

  • 高通量技术产生各种各样的omics数据 (基因组学,转录组学,表观组学,蛋白质组学).
  • 整合多学科数据对于理解复杂的特征和疾病至关重要.
  • 在数据层中识别共享的生物标志物是一个重大挑战.

研究的目的:

  • 开发一个先进的框架,用于从多学科数据中进行整合性变量选择.
  • 通过在各种数据类型中有效识别关键共享特征来增强生物标志物发现.
  • 改善复杂分子数据的生物学和临床解释.

主要方法:

  • 开发了一个基于多变量随机森林 (MRF) 的框架.
  • 一个新的逆最小深度 (IMD) 度量被用于预测器排名.
  • 该方法将响应变量分配给树节点以进行增强的特征选择.
  • 进行了对癌症基因组图谱 (TCGA) 多组数据集的模拟和分析.

主要成果:

  • 基于MRF-MRF的框架与IMD在现有整合技术上表现出优越的性能.
  • 该方法成功地确定了具有生物学意义的生物标志物和途径.
  • 选择的生物标志物与已知的生物网络和患者分层能力相关.
  • 该方法有效地处理多维数据中的高维度和噪声.

结论:

  • 开发的基于MRF的框架为多omics生物标志物发现提供了强大的和可扩展的解决方案.
  • 该方法有助于识别临床相关的生物标志物,有助于患者分层.
  • 这种方法推进了综合性的多学科分析,加速了生物和临床见解.