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A survey on deep learning in DNA/RNA motif mining

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...

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Autores principales: He, Ying, Shen, Zhen, Zhang, Qinhu, Wang, Siguo, Huang, De-Shuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293829/
https://www.ncbi.nlm.nih.gov/pubmed/33005921
http://dx.doi.org/10.1093/bib/bbaa229
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author He, Ying
Shen, Zhen
Zhang, Qinhu
Wang, Siguo
Huang, De-Shuang
author_facet He, Ying
Shen, Zhen
Zhang, Qinhu
Wang, Siguo
Huang, De-Shuang
author_sort He, Ying
collection PubMed
description 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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spelling pubmed-82938292021-07-22 A survey on deep learning in DNA/RNA motif mining He, Ying Shen, Zhen Zhang, Qinhu Wang, Siguo Huang, De-Shuang Brief Bioinform Method Review 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. Oxford University Press 2020-10-02 /pmc/articles/PMC8293829/ /pubmed/33005921 http://dx.doi.org/10.1093/bib/bbaa229 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Method Review
He, Ying
Shen, Zhen
Zhang, Qinhu
Wang, Siguo
Huang, De-Shuang
A survey on deep learning in DNA/RNA motif mining
title A survey on deep learning in DNA/RNA motif mining
title_full A survey on deep learning in DNA/RNA motif mining
title_fullStr A survey on deep learning in DNA/RNA motif mining
title_full_unstemmed A survey on deep learning in DNA/RNA motif mining
title_short A survey on deep learning in DNA/RNA motif mining
title_sort survey on deep learning in dna/rna motif mining
topic Method Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293829/
https://www.ncbi.nlm.nih.gov/pubmed/33005921
http://dx.doi.org/10.1093/bib/bbaa229
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