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This paper presents a novel ensemble learning method for designing single-guide RNAs (sgRNAs) in CRISPR applications, addressing challenges in predicting on-target efficacy and off-target sensitivity. The proposed method improves generalizability and accuracy by combining multiple machine learning models, outperforming existing techniques on benchmark datasets. This advancement has significant implications for enhancing the effectiveness and safety of CRISPR-based treatments in clinical settings.

Author

leungdoudou122

Language

EN