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.

本研究解决了通过DNA甲基化模式准确预测生物年龄的问题，特别是应对表观遗传相关漂移和CpG异质性带来的挑战。提出了一种新颖的两阶段算法，结合相似性搜索和可解释的提升机，显著提高了预测准确性并揭示了与年龄相关的CpG位点，具有重要的衰老研究意义。

iTARGET：可解释的针对分组表观遗传特征的年龄回归技术