Telethon Kids Institute, University of Western Australia, Nedlands, Perth, Western Australia.
School of Medicine, Notre Dame University, Fremantle, Western Australia.
Clin Epigenetics. 2020 Apr 3;12(1):51. 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.
机器学习是人工智能的一个分支，它利用大数据集对未来事件进行预测。尽管机器学习中使用的大多数算法早在 20 世纪 50 年代就已开发出来，但大数据的出现加上计算能力的大幅提高，在过去二十年中重新激发了人们对这项技术的兴趣。
在医学领域，机器学习在开发辅助临床工具方面具有广阔的前景，例如癌症检测和疾病预测。深度学习技术是机器学习的一个分支，它需要较少的用户输入，但需要更多的数据和处理能力，最近在辅助医生进行准确诊断方面提供了更大的希望。在遗传学及其子领域表观遗传学领域，这两个都是复杂数据的典型例子，机器学习方法的应用正在增加，因为个性化医疗领域的目标是根据个体的遗传和表观遗传特征进行治疗。
我们现在已经在疾病中发现了越来越多的报道的表观遗传改变，这为提高未来诊断和治疗的敏感性和特异性提供了机会。目前，使用机器学习应用于表观遗传学的研究有限。它们涉及到各种各样的疾病状态，并且主要使用了有监督的机器学习方法。
