{"title":"Modeling Methods and Influencing Factors for Age Estimation Based on DNA Methylation.","authors":"Yi-Hang Huang, Wei-Bo Liang, Hui Jian, Sheng-Qiu Qu","doi":"10.12116/j.issn.1004-5619.2023.530106","DOIUrl":null,"url":null,"abstract":"<p><p>Age estimation based on tissues or body fluids is an important task in forensic science. The changes of DNA methylation status with age have certain rules, which can be used to estimate the age of the individuals. Therefore, it is of great significance to discover specific DNA methylation sites and develop new age estimation models. At present, statistical models for age estimation have been developed based on the rule that DNA methylation status changes with age. The commonly used models include multiple linear regression model, multiple quantile regression model, support vector machine model, artificial neural network model, random forest model, etc. In addition, there are many factors that affect the level of DNA methylation, such as the tissue specificity of methylation. This paper reviews these modeling methods and influencing factors for age estimation based on DNA methylation, with a view to provide reference for the establishment of age estimation models.</p>","PeriodicalId":15899,"journal":{"name":"Journal of Forensic Medicine","volume":"39 6","pages":"601-607"},"PeriodicalIF":0.0000,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forensic Medicine","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.12116/j.issn.1004-5619.2023.530106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Age estimation based on tissues or body fluids is an important task in forensic science. The changes of DNA methylation status with age have certain rules, which can be used to estimate the age of the individuals. Therefore, it is of great significance to discover specific DNA methylation sites and develop new age estimation models. At present, statistical models for age estimation have been developed based on the rule that DNA methylation status changes with age. The commonly used models include multiple linear regression model, multiple quantile regression model, support vector machine model, artificial neural network model, random forest model, etc. In addition, there are many factors that affect the level of DNA methylation, such as the tissue specificity of methylation. This paper reviews these modeling methods and influencing factors for age estimation based on DNA methylation, with a view to provide reference for the establishment of age estimation models.
根据组织或体液估计年龄是法医学的一项重要任务。DNA 甲基化状态随年龄的变化有一定的规律,可用于估计个体的年龄。因此,发现特定的 DNA 甲基化位点和开发新的年龄估计模型具有重要意义。目前,人们根据 DNA 甲基化状态随年龄变化的规律,建立了年龄估计的统计模型。常用的模型包括多元线性回归模型、多元量子回归模型、支持向量机模型、人工神经网络模型、随机森林模型等。此外,影响 DNA 甲基化水平的因素还有很多,如甲基化的组织特异性等。本文综述了这些基于DNA甲基化的年龄估计建模方法和影响因素,以期为年龄估计模型的建立提供参考。