A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator.

ABSTRACT:

Background

The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation an

SUBMITTER: Doherty T 

PROVIDER: S-EPMC10152624 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature