Deep learning in bioinformatics
Deep learning in bioinformatics
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DOI：
10.1093/bib/bbw068
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发表时间：
2017-09-01
影响因子：
9.5
通讯作者：
Yoon, Sungroh
中科院分区：
文献类型：
--
作者：
Min, Seonwoo;Lee, Byunghan;Yoon, Sungroh
在大数据时代，将生物医学大数据转化为有价值的知识是生物信息学最重要的挑战之一。自21世纪初以来，深度学习发展迅速，目前已在多个领域展现出最先进的性能。因此，深度学习在生物信息学中的应用以从数据中获得洞察力在学术界和工业界都得到了强调。在这里，我们回顾了生物信息学中的深度学习，并给出了当前研究的例子。为了提供一个有用和全面的视角，我们将研究分为生物信息学领域（即组学，生物医学成像，生物医学信号处理）和深度学习架构（即深度神经网络，卷积神经网络，递归神经网络，紧急架构），并对每项研究进行简要描述。此外，我们还讨论了生物信息学中深度学习的理论和实践问题，并提出了未来的研究方向。我们相信，这篇综述将提供有价值的见解，并作为研究人员在生物信息学研究中应用深度学习方法的起点。
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-theart performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.