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Deep learning in omics: a survey and guideline.
Briefings in Functional Genomics ( IF 4 ) Pub Date : 2019-02-14 , DOI: 10.1093/bfgp/ely030
Zhiqiang Zhang 1 , Yi Zhao 2 , Xiangke Liao 1 , Wenqiang Shi 1 , Kenli Li 3 , Quan Zou 4 , Shaoliang Peng 1, 3
Affiliation  

Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fortunately, deep learning technology can contribute toward resolving these challenges. There is evidence that deep learning can handle omics data well and resolve omics problems. This survey aims to provide an entry-level guideline for researchers, to understand and use deep learning in order to solve omics problems. We first introduce several deep learning models and then discuss several research areas which have combined omics and deep learning in recent years. In addition, we summarize the general steps involved in using deep learning which have not yet been systematically discussed in the existent literature on this topic. Finally, we compare the features and performance of current mainstream open source deep learning frameworks and present the opportunities and challenges involved in deep learning. This survey will be a good starting point and guideline for omics researchers to understand deep learning.

中文翻译:

组学中的深度学习:调查和指南。

诸如基因组学,转录组和蛋白质组学之类的组学已受到大数据时代的影响。大量的高维和复杂的结构化数据使其不再适用于常规机器学习算法。幸运的是,深度学习技术可以帮助解决这些挑战。有证据表明深度学习可以很好地处理组学数据并解决组学问题。该调查旨在为研究人员提供入门级指南,以理解和使用深度学习来解决组学问题。我们首先介绍几种深度学习模型,然后讨论近年来结合了组学和深度学习的几个研究领域。此外,我们总结了使用深度学习所涉及的一般步骤,而有关该主题的现有文献尚未对此进行系统地讨论。最后,我们比较了当前主流开源深度学习框架的功能和性能,并提出了深度学习所涉及的机遇和挑战。这项调查将成为组学研究人员了解深度学习的良好起点和指南。
更新日期:2019-11-01
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