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相关概念视频

The Effect of Aging on Tissues01:19

The Effect of Aging on Tissues

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Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
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相关实验视频

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Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging
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色:一种机器学习方法,用于从转录组数据中建模组织特异性衰老.

Wasif Jalal1, Mubasshira Musarrat1, Md Abul Hassan Samee2

  • 1Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka 1205, Bangladesh.

Briefings in bioinformatics
|March 2, 2026
PubMed
概括
此摘要是机器生成的。

这项研究使用机器学习对转录基因数据进行组织特异生物年龄模型. 特定组织的加速衰老与增加的死亡风险相关,突出显示了转录组学.

关键词:
标签: 标签: GTEx 标签: 美国在PLS回归过程中,回归的结果是:年龄差距的差距.老化的老化 衰老的老化生物标志物 生物标志物不同表达的基因.组合学习组合学习线性建模线性建模机器学习是机器学习.翻译学 翻译学 翻译学 翻译学

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 衰老研究研究 衰老研究

背景情况:

  • 衰老是一种影响健康和疾病的基本生物过程.
  • 组织特异性衰老和死亡率之间的关系尚不清楚.

研究的目的:

  • 使用机器学习建模组织特定的生物年龄.
  • 开发一个"年龄差距"指标来量化从时间学年龄的偏差.
  • 调查组织特异性衰老模式与死亡率之间的相关性.

主要方法:

  • 利用了来自12种组织类型的GTEx转录数据.
  • 应用机器学习模型 (皮尔森相关性,弹性净回归,神经网络).
  • 采用特征选择策略,包括皮尔森相关性,与年龄相关的差异表达基因和组织丰富基因.

主要成果:

  • 开发了精确的模型来预测特定组织的生物年龄 (例如,平均年龄). RMSE 6.44 年,R2 0.64).
  • 确定了显著的组织特异性衰老模式和"极端衰老者" (20%在一个组织中,1%在多个组织中).
  • 在特定组织中发现加速衰老与较高的疾病死亡风险相关.

结论:

  • 转录数据和机器学习可以有效地模拟特定组织的生物衰老.
  • 特定组织的衰老模式和"年龄差距"与死亡率有关.
  • 这些发现强调了转录学在理解衰老和长寿方面的重要性.