Document Type
Theses, Ph.D
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, 3. MEDICAL AND HEALTH SCIENCES
Abstract
Epigenetic modifications can lead to altered phenotypes without a change in the DNA sequence itself. Disrupted gene expression regulated by epigenetic processes can result in cancers, autoimmune diseases and various other maladies. Machine learning (ML) involves the use of algorithms and models which are trained to learn patterns in data, and has demonstrated remarkable success in solving diverse, complex challenges. Epigenomic studies, such as those that use DNA methylation (DNAm) data, increasingly make use of ML techniques to process extremely high dimensional data obtained from high throughput platforms e.g., DNAm arrays. These datasets suffer from the curse of dimensionality, increased computational complexity and are prone to overfitting – making feature reduction techniques critical.
DOI
https://doi.org/10.21427/83pk-nh15
Recommended Citation
Doherty, Trevor, "Machine Learning Applications in Epigenomics and its Association with Health and Disease" (2025). Doctoral. 286.
https://arrow.tudublin.ie/sciendoc/286
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
A thesis submitted for the degree of Doctor of Philosophy, School of Biological, Health and Sports Sciences, November 2024.
doi:10.21427/83pk-nh15