A survey on deep learning in DNA/RNA motif mining.
A survey on deep learning in DNA/RNA motif mining.
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DNA/RNA 基序挖掘中的深度学习综述
DOI：
10.1093/bib/bbaa229
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发表时间：
2021-07-20
影响因子：
9.5
通讯作者：
Huang DS
中科院分区：
文献类型：
--
作者：
He Y;Shen Z;Zhang Q;Wang S;Huang DS
DNA/RNA基序挖掘是基因功能研究的基础。DNA/RNA基序挖掘在识别DNA或RNA-蛋白结合位点中起着极其重要的作用，有助于了解基因调控和管理的机制。在过去的几十年里，研究人员一直致力于设计新的高效、准确的motif挖掘算法。这些算法大致可分为两类：枚举法和概率法。近年来，机器学习方法取得了很大的进步，特别是以深度学习为代表的算法取得了很好的性能。现有的motif挖掘深度学习方法大致可分为三类模型：基于卷积神经网络（CNN）的模型、基于循环神经网络（RNN）的模型和基于CNN - RNN混合模型。我们从数据预处理、现有深度学习架构的特点以及比较基本深度学习模型之间的差异等方面介绍了深度学习在motif mining领域的应用。通过对现有深度学习方法的分析和比较，我们发现在数据充足的情况下，越复杂的模型往往比简单的模型表现得更好，而目前的方法与计算机视觉、语言处理（NLP）、计算机游戏等其他领域相比，也相对简单。因此，有必要对基于深度学习的motif挖掘进行总结，以帮助研究者更好地理解这一领域。
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and management. For the past few decades, researchers have been working on designing new efficient and accurate algorithms for mining motif. These algorithms can be roughly divided into two categories: the enumeration approach and the probabilistic method. In recent years, machine learning methods had made great progress, especially the algorithm represented by deep learning had achieved good performance. Existing deep learning methods in motif mining can be roughly divided into three types of models: convolutional neural network (CNN) based models, recurrent neural network (RNN) based models, and hybrid CNN–RNN based models. We introduce the application of deep learning in the field of motif mining in terms of data preprocessing, features of existing deep learning architectures and comparing the differences between the basic deep learning models. Through the analysis and comparison of existing deep learning methods, we found that the more complex models tend to perform better than simple ones when data are sufficient, and the current methods are relatively simple compared with other fields such as computer vision, language processing (NLP), computer games, etc. Therefore, it is necessary to conduct a summary in motif mining by deep learning, which can help researchers understand this field.
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影响因子：
3.7
作者：
Asgari E;Mofrad MR
通讯作者：
Mofrad MR
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作者：
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通讯作者：
Stegle O
影响因子：
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作者：
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通讯作者：
Westhead, DR
影响因子：
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作者：
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通讯作者：
Akalin A