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        <title>Prediction of CRISPR On-Target Effects via Deep Learning</title>
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    <name type="personal">
        <namePart type="given">Condy</namePart>
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        <namePart type="given">Fuxiao</namePart>
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            <namePart type="given">Lotem</namePart>
            <namePart type="family">Peled-Cohen</namePart>
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    <abstract>Since the advent of CRISPR-Cas9, a groundbreaking gene-editing technology that enables precise genomic modifications via a short RNA guide sequence, there has been a marked increase in the accessibility and application of this technology across various fields. The success of CRISPR-Cas9 has spurred further investment and led to the discovery of additional CRISPR systems, including CRISPR-Cas13. Distinct from Cas9, which targets DNA, Cas13 targets RNA, offering unique advantages for gene modulation. We focus on Cas13d, a variant known for its collateral activity where it non-specifically cleaves adjacent RNA molecules upon activation, a feature critical to its function. We introduce DeepFM-Crispr, a novel deep learning model developed to predict the on-target efficiency and evaluate the off-target effects of Cas13d. This model harnesses a large language model to generate comprehensive representations rich in evolutionary and structural data, thereby enhancing predictions of RNA secondary structures and overall sgRNA efficacy. A transformer-based architecture processes these inputs to produce a predictive efficacy score. Comparative experiments show that DeepFM-Crispr not only surpasses traditional models but also outperforms recent state-of-the-art deep learning methods in terms of prediction accuracy and reliability.</abstract>
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            <start>9</start>
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%0 Conference Proceedings
%T Prediction of CRISPR On-Target Effects via Deep Learning
%A Bao, Condy
%A Liu, Fuxiao
%Y Peled-Cohen, Lotem
%Y Calderon, Nitay
%Y Lissak, Shir
%Y Reichart, Roi
%S Proceedings of the 1st Workshop on NLP for Science (NLP4Science)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F bao-liu-2024-prediction
%X Since the advent of CRISPR-Cas9, a groundbreaking gene-editing technology that enables precise genomic modifications via a short RNA guide sequence, there has been a marked increase in the accessibility and application of this technology across various fields. The success of CRISPR-Cas9 has spurred further investment and led to the discovery of additional CRISPR systems, including CRISPR-Cas13. Distinct from Cas9, which targets DNA, Cas13 targets RNA, offering unique advantages for gene modulation. We focus on Cas13d, a variant known for its collateral activity where it non-specifically cleaves adjacent RNA molecules upon activation, a feature critical to its function. We introduce DeepFM-Crispr, a novel deep learning model developed to predict the on-target efficiency and evaluate the off-target effects of Cas13d. This model harnesses a large language model to generate comprehensive representations rich in evolutionary and structural data, thereby enhancing predictions of RNA secondary structures and overall sgRNA efficacy. A transformer-based architecture processes these inputs to produce a predictive efficacy score. Comparative experiments show that DeepFM-Crispr not only surpasses traditional models but also outperforms recent state-of-the-art deep learning methods in terms of prediction accuracy and reliability.
%U https://aclanthology.org/2024.nlp4science-1.2
%P 9-15

Markdown (Informal)

[Prediction of CRISPR On-Target Effects via Deep Learning](https://aclanthology.org/2024.nlp4science-1.2) (Bao & Liu, NLP4Science 2024)

• Prediction of CRISPR On-Target Effects via Deep Learning (Bao & Liu, NLP4Science 2024)

ACL

• Condy Bao and Fuxiao Liu. 2024. Prediction of CRISPR On-Target Effects via Deep Learning. In Proceedings of the 1st Workshop on NLP for Science (NLP4Science), pages 9–15, Miami, FL, USA. Association for Computational Linguistics.