基于甲基化数据精准预测生物年龄，还能解释关键基因位点变化
iTARGET: Interpretable Tailored Age Regression for Grouped Epigenetic Traits
- 分阶段处理：先按年龄分组聚类，再用可解释模型逐组预测
- 比传统时钟更准，能识别出与年龄相关的关键甲基化位点
- 适合关注衰老机制或需要模型可解释性的研究人员
从DNA甲基化模式中准确预测生理年龄对推进生物年龄评估至关重要。然而，表观遗传相关性漂移（ECD）和CpG位点间异质性（HAC）使得这一任务面临挑战，它们反映了不同生命阶段甲基化与年龄之间的动态关系。为此，我们提出一种两阶段新算法：第一阶段通过相似性搜索按年龄分组聚类甲基化谱；第二阶段采用可解释梯度提升机（EBM）实现组内精准预测。实验表明，该方法不仅提升预测精度，还能揭示关键年龄相关CpG位点，检测年龄特异性衰老速率变化，并识别CpG位点间的配对交互作用，优于传统表观遗传时钟和机器学习模型，为衰老研究提供更准确、可解释的解决方案。
原文摘要 · Abstract (English)
Accurately predicting chronological age from DNA methylation patterns is crucial for advancing biological age estimation. However, this task is made challenging by Epigenetic Correlation Drift (ECD) and Heterogeneity Among CpGs (HAC), which reflect the dynamic relationship between methylation and age across different life stages. To address these issues, we propose a novel two-phase algorithm. The first phase employs similarity searching to cluster methylation profiles by age group, while the second phase uses Explainable Boosting Machines (EBM) for precise, group-specific prediction. Our method not only improves prediction accuracy but also reveals key age-related CpG sites, detects age-specific changes in aging rates, and identifies pairwise interactions between CpG sites. Experimental results show that our approach outperforms traditional epigenetic clocks and machine learning models, offering a more accurate and interpretable solution for biological age estimation with significant implications for aging research.
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