@inproceedings{bao-liu-2024-prediction,
    title = "Prediction of {CRISPR} On-Target Effects via Deep Learning",
    author = "Bao, Condy  and
      Liu, Fuxiao",
    editor = "Peled-Cohen, Lotem  and
      Calderon, Nitay  and
      Lissak, Shir  and
      Reichart, Roi",
    booktitle = "Proceedings of the 1st Workshop on NLP for Science (NLP4Science)",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2024.nlp4science-1.2/",
    doi = "10.18653/v1/2024.nlp4science-1.2",
    pages = "9--15",
    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."
}