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相关概念视频

Epigenetic Regulation01:37

Epigenetic Regulation

3.0K
Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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使用可解释的机器学习框架探索DNA甲基化和生物年龄之间的相关性.

Sheng Zhou1, Jing Chen2, Shanshan Wei1

  • 1Department of Public Health and Health, Guizhou Medical University, Guizhou Province, China.

Scientific reports
|October 15, 2024
PubMed
概括

随着年龄的增长,DNA甲基化系统地发生变化,可以预测生物年龄. 机器学习识别了关键的甲基化位点,如cg23995914,并探索了它们的生物学意义.

关键词:
生物年龄 生物年龄通过DNA甲基化.在GO上进行缩分析.可以解释的机器学习.沙普利的添加式扩展 扩展.在XGBoost中使用.

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Optimized Analysis of DNA Methylation and Gene Expression from Small, Anatomically-defined Areas of the Brain
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科学领域:

  • 表观遗传学和基因组学
  • 计算生物学 计算生物学
  • 衰老研究研究 衰老研究

背景情况:

  • DNA甲基化对于基因调节至关重要,并且随着年龄的增长而有预测性的变化.
  • 这些与年龄相关的甲基化模式为生物年龄的预测提供了潜力.
  • 了解这些表观遗传标记是衰老研究的关键.

研究的目的:

  • 为了确定与生物年龄相关的特定DNA甲基化位点.
  • 使用机器学习开发生物年龄的预测模型.
  • 探索与年龄相关的甲基化标记物的生物功能.

主要方法:

  • 利用了人类甲基化数据,经过预处理和特征选择.
  • 应用机器学习算法 (XGBoost,LightGBM,CatBoost) 用于模型构建.
  • 在15个数据集上使用SHAP,基因本体学 (GO) 丰富和KEGG途径分析进行了深入分析.

主要成果:

  • 确定了与生物年龄相关的15个不同的甲基化位点组.
  • 通过SHAP值,cg23995914位点被确定为生物年龄的最有影响力的预测因素.
  • GO和KEGG的分析提供了对这些甲基化位点的生物学作用的初步见解.

结论:

  • 建立了基于DNA甲基化进行生物年龄预测的强大的机器学习模型.
  • 突出了特定甲基化位点,特别是cg23995914在衰老中的重要性.
  • 证明了表观遗传标记对于理解衰老过程及其分子基础的有用性.