Accurate determination of the sample’s chronological age is an important forensic problem. This regression problem may be improved by selecting appropriate methylomic features. Most of the existing feature selection algorithms, however, optimize the regression performance by considering only the original features. This study proposed four feature engineering strategies to transform the original methylomic features. The regression performance of the age regression model was improved by the resampling-based feature selection algorithm FeSTwo proposed in this study. FeSTwo outperformed the parallel algorithms used in the previous studies even with the electronic health record data. The age prediction performance of the FeSTwo-detected features was also confirmed for another independent dataset. The study results demonstrated that the proposed model, FeSTwo, led to a more than 8% reduction in root-mean-square error (RMSE) on the test dataset with only 70 features.

准确确定样品的年代年龄是重要的法医学问题。通过选择适当的甲基化特征可以改善此回归问题。但是，大多数现有特征选择算法仅通过考虑原始特征来优化回归性能。这项研究提出了四种特征工程策略来转化原始的甲基化特征。这项研究中提出的基于重采样的特征选择算法FeSTwo改善了年龄回归模型的回归性能。即使使用电子健康记录数据，FeSTwo的性能也优于先前研究中使用的并行算法。FeSTwo检测到的特征的年龄预测性能也被另一个独立的数据集证实。研究结果表明，提出的模型