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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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. |
format | Online Article Text |
id | pubmed-8293829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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