Deep learning in bioinformatics: Introduction, application, and perspective in the big data era
Deep learning in bioinformatics: Introduction, application, and perspective in the big data era
复制标题
DOI：
10.1016/j.ymeth.2019.04.008
复制
发表时间：
2019-08-15
期刊：
影响因子：
4.8
通讯作者：
Gao, Xin
中科院分区：
文献类型：
--
作者：
Li, Yu;Huang, Chao;Gao, Xin
深度学习在处理大数据方面尤其强大，在包括生物信息学在内的各个领域都取得了巨大成功。随着生物学大数据时代的发展，可以预见深度学习将在该领域变得越来越重要，并将被纳入绝大多数分析管道中。在这篇综述中，我们提供了深度学习的开放式介绍，以及其在生物信息学中代表性应用的具体示例和实现。本文从深度学习在生物信息学领域的最新研究成果出发，指出了适合深度学习的问题。之后，我们以易于理解的方式介绍深度学习，从浅层神经网络到传奇的卷积神经网络，传奇的递归神经网络，图神经网络，生成对抗网络，变分自动编码器以及最新的最先进的架构。之后，我们提供了八个例子，涵盖了五个生物信息学研究方向和所有四种数据类型，并使用Tensorflow和Keras编写了实现。最后，我们讨论了用户在采用深度学习方法时会遇到的常见问题，如过拟合和可解释性，并提供了相应的建议。
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions.