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

Associations of proteomic age clocks with lifestyle risk factors, incident chronic diseases and mortality in two European cohorts.

Nature aging·2026
Same author

The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome.

Nature aging·2026
Same author

A lipidomic based metabolic age score for monitoring the effects of lifestyle and diet on metabolic disease risk.

Research square·2026
Same author

Exploring sex-specific causal links between thousands of proteins and lipid metabolism using the UK Biobank Pharma Proteomics Project data.

Biology of sex differences·2026
Same author

Prediagnostic serum iodine and selenium in relation to breast cancer survival.

BJS open·2026
Same author

Circulating lipids are related to longitudinal changes of ATN biomarkers for Alzheimer's disease.

Molecular psychiatry·2026

相关实验视频

Updated: Jan 15, 2026

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation
07:12

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Published on: April 24, 2020

10.5K

通过机器学习引导血蛋白水平的解卷.

Maik Pietzner1,2,3, Carl Beuchel4,5, Kamil Demircan4,6

  • 1Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. maik.pietzner@bih-charite.de.

Molecular systems biology
|October 9, 2025
PubMed
概括

机器学习确定了影响超过43,000个人的血液蛋白质水平的关键因素. 可修改的因素解释了比遗传学更多的蛋白质变异,有助于生物标志物的发现.

关键词:
生物标志物生物标志物药物 药物 药物 是一种药物.丰富方式 丰富方式血蛋白质组学是什么意思

更多相关视频

Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum
09:22

Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum

Published on: April 13, 2018

17.3K
Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI
08:57

Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI

Published on: August 18, 2017

8.3K

相关实验视频

Last Updated: Jan 15, 2026

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation
07:12

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Published on: April 24, 2020

10.5K
Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum
09:22

Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum

Published on: April 13, 2018

17.3K
Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI
08:57

Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI

Published on: August 18, 2017

8.3K

科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 生物标志物发现发现
  • 机器学习 机器学习

背景情况:

  • 蛋白质组技术测量数千种血液蛋白质,但了解它们的起源对于生物标志物开发至关重要.
  • 对影响血蛋白水平的因素的有限知识阻碍了临床转化.

研究的目的:

  • 使用机器学习系统地识别参与者和样本特征,以解释血蛋白水平的差异.
  • 开发一个框架来识别潜在的药物向参与标记物和疾病特异性生物标记物.

主要方法:

  • 应用机器学习分析了43,240个人的血蛋白水平和>1800名参与者/样本特征.
  • 基于共享的解释因素的聚类蛋白质,并将发现与遗传和药物数据集成到知识图中.

主要成果:

  • 20个因素的中位数解释了19.4%的蛋白质变异,可改变的因素 (10.0%) 比遗传变异 (3.9%) 贡献更多.
  • 解释因素在性别和祖先群体中基本一致.
  • 确定了矩阵金属蛋白酶12作为腹腔大动脉动脉瘤的潜在生物标志物.

结论:

  • 机器学习可以系统地发现驱动血蛋白水平的因素,推进生物标志物发现.
  • 开发的框架和知识图有助于识别蛋白质生物标记物和潜在的药物标.
  • 该资源使表型丰富成为可能,并为探索蛋白质组数据和发现提供了工具.