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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\u2013RNN 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.<\/jats:p>","DOI":"10.1093\/bib\/bbaa229","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T11:09:30Z","timestamp":1598440170000},"source":"Crossref","is-referenced-by-count":94,"title":["A survey on deep learning in DNA\/RNA motif mining"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9592-7727","authenticated-orcid":false,"given":"Ying","family":"He","sequence":"first","affiliation":[{"name":"computer science and technology at Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Shen","sequence":"additional","affiliation":[{"name":"computer science and technology at Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"computer science and technology at Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siguo","family":"Wang","sequence":"additional","affiliation":[{"name":"computer science and technology at Tongji University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"De-Shuang","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Machines Learning and Systems Biology, Tongji University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,10,2]]},"reference":[{"key":"2021072112100403000_ref1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1093\/bib\/bbv031","article-title":"Revealing protein\u2013lncRNA interaction","volume":"17","author":"Ferre","year":"2016","journal-title":"Brief Bioinform"},{"key":"2021072112100403000_ref2","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1038\/nrg3813","article-title":"A census of human RNA-binding proteins","volume":"15","author":"Gerstberger","year":"2014","journal-title":"Nat Rev Genet"},{"key":"2021072112100403000_ref3","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.molcel.2011.11.026","article-title":"Scd6 targets eIF4G to repress translation: RGG motif proteins as a class of eIF4G-binding proteins","volume":"45","author":"Rajyaguru","year":"2012","journal-title":"Mol 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