A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator | 2023
Doherty, Trevor Dempster, Emma Hannon, Eilis Mill, Jonathan Poulton, ... Show all » Richie Corcoran, David Sugden, Karen Williams, Ben Caspi, Avshalom Moffitt, Terrie E. Delany, Sarah Jane Murphy, Therese M. « Hide
BMC Bioinformatics, 2023, 24(1), 178.
10.1186/s12859-023-05282-4
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Our ref: RO830
Show abstract » 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 and two independent test sets for the comparisons.
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