标题： iTARGET：可解释的定制年龄回归用于分组表观遗传特征 显示英文标题

标题： iTARGET: Interpretable Tailored Age Regression for Grouped Epigenetic Traits

摘要： 从DNA甲基化模式准确预测 chronological age 对于推进生物年龄估计至关重要。 然而，表观遗传相关性漂移（ECD）和CpGs之间的异质性（HAC）使这项任务变得具有挑战性，它们反映了不同生命阶段甲基化与年龄之间的动态关系。 为了解决这些问题，我们提出了一种新的两阶段算法。 第一阶段使用相似性搜索按年龄组对甲基化图谱进行聚类，第二阶段使用可解释增强机（EBM）进行精确的、组特定的预测。 我们的方法不仅提高了预测准确性，还揭示了关键的与年龄相关的CpG位点，检测了衰老速率的年龄特异性变化，并识别了CpG位点之间的成对相互作用。 实验结果表明，我们的方法优于传统的表观遗传时钟和机器学习模型，为生物年龄估计提供了一个更准确且可解释的解决方案，对衰老研究具有重要意义。 显示英文摘要

摘要： 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.