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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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Autor principal: Lee, Minhyeok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
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
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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.
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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
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