Holder Lawrence B, Haque M Muksitul, Skinner Michael K
a School of Electrical Engineering and Computer Science , Washington State University , Pullman , WA , USA.
b Center for Reproductive Biology, School of Biological Sciences , Washington State University , Pullman , WA , USA.
Epigenetics. 2017 Jul 3;12(7):505-514. doi: 10.1080/15592294.2017.1329068. Epub 2017 May 19.
Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.
了解表观遗传过程在医学应用方面具有巨大潜力。机器学习（ML）的进展对于实现这一潜力至关重要。先前的研究使用了与疾病表观遗传跨代遗传的种系传递相关的表观遗传数据集以及新颖的ML方法来预测关键表观突变的全基因组位置。主动学习（ACL）和不平衡类学习（ICL）相结合，用于解决ML过去存在的问题，以开发更有效的特征选择过程，并解决所有基因组数据集中的不平衡问题。本文展示了这种新颖的ML方法的强大功能以及我们预测表观遗传现象和相关疾病的能力。当前的方法需要对基因组进行大量的特征计算。一种有前途的新方法是引入深度学习（DL），用于生成并同时计算针对分类任务进行调整的新型基因组特征。这种方法可用于应用于医学的任何基因组或生物数据集。分子表观遗传数据在先进机器学习分析在医学中的应用是本综述的重点。
