Department of Public Health and Health, Guizhou Medical University, Guizhou Province, China.
Guizhou Provincial Drug Administration inspection center, Guiyang, Guizhou Province, China.
Sci Rep. 2024 Oct 15;14(1):24208. doi: 10.1038/s41598-024-75586-9.
DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual's age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci's biological significance.
DNA 甲基化在转录调控中起着重要作用，并随着年龄的增长呈现系统性变化。这些变化可用于预测个体的年龄。首先，确定与生物年龄相关的甲基化位点；其次，构建生物年龄预测模型，并使用机器学习初步探讨甲基化相关基因的生物学意义。通过数据预处理、特征选择程序、统计分析和机器学习技术，使用人类甲基化数据构建了生物年龄预测模型。随后，使用 SHAP、GO 富集和 KEGG 分析对 15 个甲基化数据集进行了深入分析。XGBoost、LightGBM 和 CatBoost 确定了 15 组与生物年龄相关的甲基化位点。通过计算 SHAP 值，确定 cg23995914 位点是预测生物年龄的最重要贡献者。此外，还进行了 GO 富集和 KEGG 分析，初步探讨了甲基化位点的生物学意义。
