Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification

• Published in: Clinical epigenetics 2020-04, Vol.12 (1), p.51-51, Article 51
• Main Authors: Rauschert, S, Raubenheimer, K, Melton, P E, Huang, R C
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Biomarkers; Datasets; Diagnosis; Disease - classification; DNA Methylation; Epigenesis, Genetic; Epigenetic inheritance; Epigenetics; Epigenomics; Genetics; Humans; Learning algorithms; Machine Learning; Mediation; Natural language processing; Precision Medicine; Review; Supervised Machine Learning; Unsupervised Machine Learning
• ISSN: 1868-7075; 1868-7083; 1868-7083; 1868-7075
• DOI: 10.1186/s13148-020-00842-4

Machine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big data in combination with dramatically increased computing power has spurred renewed interest in this technology over the last two decades. Within the medical field, machine learning is promising in the development of assistive clinical tools for detection of e.g. cancers and prediction of disease. Recent advances in deep learning technologies, a sub-discipline of machine learning that requires less user input but more data and processing power, has provided even greater promise in assisting physicians to achieve accurate diagnoses. Within the fields of genetics and its sub-field epigenetics, both prime examples of complex data, machine learning methods are on the rise, as the field of personalised medicine is aiming for treatment of the individual based on their genetic and epigenetic profiles. We now have an ever-growing number of reported epigenetic alterations in disease, and this offers a chance to increase sensitivity and specificity of future diagnostics and therapies. Currently, there are limited studies using machine learning applied to epigenetics. They pertain to a wide variety of disease states and have used mostly supervised machine learning methods.