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

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc
Pages187-200
Number of pages14
DOIs
Published2022

Publication series

NameMethods in Molecular Biology
Volume2432
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Bibliographical note

Publisher Copyright: © 2022, Springer Science+Business Media, LLC, part of Springer Nature.

King's Authors

Abstract

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).

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