iTARGET：面向分组表观遗传性状的可解释定制化年龄回归
iTARGET: Interpretable Tailored Age Regression for Grouped Epigenetic Traits
AI总结：
本文提出两阶段 iTARGET 算法，先按年龄组聚类甲基化谱，再用 EBM 进行可解释的组特异性年龄回归，在准确性和生物学解释上优于传统表观遗传时钟。
摘要：
从 DNA 甲基化模式准确预测实际年龄，对于推进生物学年龄估计至关重要。然而，表观遗传相关漂移（Epigenetic Correlation Drift，ECD）以及 CpG 位点间异质性（Heterogeneity Among CpGs，HAC）使这项任务具有挑战性，二者反映了不同生命阶段中甲基化与年龄之间的动态关系。为解决这些问题，我们提出一种新颖的两阶段算法。第一阶段采用相似性搜索，按年龄组对甲基化谱进行聚类；第二阶段使用可解释提升机（Explainable Boosting Machines，EBM）进行精确的、针对特定组的预测。我们的方法不仅提高了预测准确性，还揭示关键的年龄相关 CpG 位点，检测衰老速率中特定年龄的变化，并识别 CpG 位点之间的成对交互作用。实验结果表明，该方法优于传统表观遗传时钟和机器学习模型，为生物学年龄估计提供了更准确、更具可解释性的解决方案，并对衰老研究具有重要意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
