De Riso Giulia, Cocozza Sergio
Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Naples, Italy.
Curr Med Chem. 2021;28(32):6654-6674. doi: 10.2174/0929867327666201117142006.
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.
表观遗传学是生物科学的一个领域，专注于研究基因功能中可逆的、可遗传的变化，这些变化并非由基因组序列的修饰引起。这些变化是几种分子机制之间复杂相互作用的结果，而这种相互作用又由遗传和环境因素精心调控。表观遗传图谱捕捉了个体独特的调控格局以及对环境刺激的暴露情况。因此，它构成了个性化医疗的宝贵信息库，个性化医疗旨在根据每个人的独特特征定制医疗保健干预措施。如今，借助大规模高通量技术，可以在全基因组水平上研究表观基因组标记的复杂环境。这种新的实验方法正在开辟新的、有趣的知识视角。然而，对这些复杂的组学数据进行分析需要面对重要的分析问题。人工智能，尤其是机器学习，正成为解读表观基因组数据的强大资源。在这篇综述中，我们首先将描述表观基因组学中最常用的机器学习方法。然后，我们将概述机器学习在表观基因组分析中的一些最新应用。最后，我们将提供一些例子，说明机器学习方法处理表观遗传数据如何对个性化医疗有用。
