Document Type
Article
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE
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.
DOI
https://doi.org/10.1186/s12859-023-05282-4
Recommended Citation
Doherty, Trevor; Dempster, Emma; Hannon, Eilis; Mill, Jonathan; Poulton, Richie; Corcoran, David; Sugden, Karen; Williams, Ben; Caspi, Avshalom; Moffitt, Terrie E.; Delany, Sarah Jane; and Murphy, Therese Dr, "A Comparison of Feature Selection Methodologies and Learning Algorithms in the Development of a DNA Methylation-Based Telomere Length Estimator" (2023). Articles. 209.
https://arrow.tudublin.ie/scschcomart/209
Funder
Science Foundation Ireland under Grant number 18/CRT/6183
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Publication Details
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05282-4
Doherty, T., Dempster, E., Hannon, E. et al. A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator. BMC Bioinformatics 24, 178 (2023).
https://doi.org/10.1186/s12859-023-05282-4