Abstract
Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function, not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual.
Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, high-throughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues.
Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.
Keywords: Artificial intelligence, personalized medicine, machine learning, data mining, epigenomics, multiomics.
Current Medicinal Chemistry
Title:Artificial Intelligence for Epigenetics: Towards Personalized Medicine
Volume: 28 Issue: 32
Author(s): Giulia De Riso*Sergio Cocozza
Affiliation:
- Department of Molecular Medicine and Medical Biotechnology, University of Naples “Federico II”, Naples,Italy
Keywords: Artificial intelligence, personalized medicine, machine learning, data mining, epigenomics, multiomics.
Abstract:
Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function, not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual.
Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, high-throughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues.
Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.
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Cite this article as:
De Riso Giulia *, Cocozza Sergio , Artificial Intelligence for Epigenetics: Towards Personalized Medicine, Current Medicinal Chemistry 2021; 28 (32) . https://dx.doi.org/10.2174/0929867327666201117142006
DOI https://dx.doi.org/10.2174/0929867327666201117142006 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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