Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited Predictors

DNA甲基化 表观遗传学 甲基化 计算机科学 计算生物学 相关性 机器学习 DNA测序 生物 人工智能 数据挖掘 遗传学 DNA 数学 基因 基因表达 几何学
出处
期刊:Methods in molecular biology [Springer Science+Business Media]
卷期号:: 187-200
标识
DOI:10.1007/978-1-0716-1994-0_14
摘要

Recent research studies using epigenetic data have been exploring whether it is possible to estimate how old someone is using only their DNA. This application stems from the strong correlation that has been observed in humans between the methylation status of certain DNA loci and chronological age. While genome-wide methylation sequencing has been the most prominent approach in epigenetics research, recent studies have shown that targeted sequencing of a limited number of loci can be successfully used for the estimation of chronological age from DNA samples, even when using small datasets. Following this shift, the need to investigate further into the appropriate statistics behind the predictive models used for DNA methylation-based prediction has been identified in multiple studies. This chapter will look into an example of basic data manipulation and modeling that can be applied to small DNA methylation datasets (100–400 samples) produced through targeted methylation sequencing for a small number of predictors (10–25 methylation sites). Data manipulation will focus on converting the obtained methylation values for the different predictors to a statistically meaningful dataset, followed by a basic introduction into importing such datasets in R, as well as randomizing and splitting into appropriate training and test sets for modeling. Finally, a basic introduction to R modeling will be outlined, starting with feature selection algorithms and continuing with a simple modeling example (linear model) as well as a more complex algorithm (Support Vector Machine).
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
香蕉觅云应助青藤采纳,获得10
刚刚
研友_zLaJQn完成签到,获得积分10
1秒前
3秒前
3秒前
7秒前
可爱的函函应助lily采纳,获得30
7秒前
8秒前
10秒前
11秒前
忧伤的飞鸟完成签到,获得积分10
12秒前
翟呼呼完成签到 ,获得积分10
14秒前
你的风筝应助柔柔柔柔子采纳,获得10
14秒前
changhao6787发布了新的文献求助10
14秒前
黎雪芳完成签到,获得积分10
15秒前
15秒前
好好活着关注了科研通微信公众号
15秒前
田様应助moon采纳,获得10
16秒前
是莉莉娅完成签到,获得积分10
16秒前
Planta完成签到,获得积分10
19秒前
椰子完成签到 ,获得积分10
19秒前
nenoaowu发布了新的文献求助10
19秒前
Albertxkcj发布了新的文献求助10
19秒前
今后应助标致的雨真采纳,获得30
20秒前
洋洋完成签到 ,获得积分10
21秒前
21秒前
23秒前
五斤老陈醋完成签到,获得积分10
23秒前
nenoaowu完成签到,获得积分10
24秒前
上官若男应助费老三采纳,获得10
24秒前
Jaikaran完成签到,获得积分10
24秒前
hmm发布了新的文献求助10
25秒前
26秒前
kfwxz2022完成签到,获得积分10
26秒前
会笑的光发布了新的文献求助10
27秒前
28秒前
谷粱安卉完成签到 ,获得积分10
28秒前
归尘发布了新的文献求助10
29秒前
木木完成签到,获得积分20
30秒前
漾黎完成签到,获得积分10
31秒前
zrs完成签到,获得积分10
32秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969782
求助须知:如何正确求助?哪些是违规求助? 3514601
关于积分的说明 11174816
捐赠科研通 3249899
什么是DOI,文献DOI怎么找? 1795080
邀请新用户注册赠送积分活动 875599
科研通“疑难数据库(出版商)”最低求助积分说明 804886