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. 2022 Jul 25;16:26. doi: 10.1186/s40246-022-00396-x

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Timeline of implementing deep learning algorithms in genomics. This timeline plot demonstrated the delay of implementing DL tools in genomics; for example, both (LSTM) and (BLSTM) algorithms have been invented in 1997 and the first genomic application was implemented in 2015. Similar observations are for the rest of the deep learning algorithms (Table 6)