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Deep learning in CRISPR-Cas systems: a review of recent studies
In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352112/ https://www.ncbi.nlm.nih.gov/pubmed/37469443 http://dx.doi.org/10.3389/fbioe.2023.1226182 |
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author | Lee, Minhyeok |
author_facet | Lee, Minhyeok |
author_sort | Lee, Minhyeok |
collection | PubMed |
description | In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019–2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area. |
format | Online Article Text |
id | pubmed-10352112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103521122023-07-19 Deep learning in CRISPR-Cas systems: a review of recent studies Lee, Minhyeok Front Bioeng Biotechnol Bioengineering and Biotechnology In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019–2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area. Frontiers Media S.A. 2023-07-03 /pmc/articles/PMC10352112/ /pubmed/37469443 http://dx.doi.org/10.3389/fbioe.2023.1226182 Text en Copyright © 2023 Lee. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Lee, Minhyeok Deep learning in CRISPR-Cas systems: a review of recent studies |
title | Deep learning in CRISPR-Cas systems: a review of recent studies |
title_full | Deep learning in CRISPR-Cas systems: a review of recent studies |
title_fullStr | Deep learning in CRISPR-Cas systems: a review of recent studies |
title_full_unstemmed | Deep learning in CRISPR-Cas systems: a review of recent studies |
title_short | Deep learning in CRISPR-Cas systems: a review of recent studies |
title_sort | deep learning in crispr-cas systems: a review of recent studies |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352112/ https://www.ncbi.nlm.nih.gov/pubmed/37469443 http://dx.doi.org/10.3389/fbioe.2023.1226182 |
work_keys_str_mv | AT leeminhyeok deeplearningincrisprcassystemsareviewofrecentstudies |