评估与衰老相关的转录性变化和基于人类血液转录基因组的年龄预测模型的发展
Ivan Duran1, Amy Tsurumi2,3
1Department of Surgery, Massachusetts General Hospital and Harvard Medical School, 50 Blossom St., Boston, MA, 02114, USA.
Biogerontology
|April 4, 2025
概括
这项研究使用机器学习开发了基于血液转录基因的年龄预测模型. 像XGBoost和LightGBM这样的渐变增强方法在从基因表达数据中预测生物年龄方面表现出卓越的性能.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 衰老与DNA甲基组和蛋白质组的分子变化有关.
- 现有的老化时钟模型往往缺乏从人类血液中获取的转录基因数据.
- 机器学习算法有可能开发准确的年龄预测模型.
研究的目的:
- 开发和比较使用人类血液转录组数据的年龄预测模型.
- 识别与衰老相关的基因和生物途径.
- 为了评估不同的机器学习算法的性能,用于年龄预测.
主要方法:
- 利用了来自 10K 免疫细胞库 (年龄21-90岁) 的血液转录组数据.
- 应用机器学习算法:最小绝对收缩和选择运算符 (LASSO),弹性网 (EN),极端梯度增强 (XGBoost) 和轻梯度增强机器 (LightGBM).
- 进行了差异基因表达分析,基因本体学,通路和疾病本体学分析.
主要成果:
- 在年龄预测方面,XGBoost (142个基因) 和LightGBM (149个基因) 的表现优于LASSO (7个基因) 和EN (9个基因).
- 梯度提升模型在训练,测试和外部验证集上取得了更高的准确性.
- 确定了与衰老相关的不同调节的转录和相关的生物功能.
结论:
- 基于血液转录组的年龄预测模型为监测生物衰老提供了一种可行的方法.
- 这些模型为老化过程提供了分子洞察力.
- 建议在多种不同人群中进行进一步的外部验证和机制研究.
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