Deep Learning for CRISPR-Cas۹ Off-Target Prediction

CRISPR technology enables precise genome editing, yet unintended off-target cleavages remain a major bottleneck affecting its safety and accuracy. Accurate computational prediction of these off-target sites is essential for minimizing risks and improving experimental design. To address this, we present an abstract summarizing a deep learning framework that leverages a multi-branch transformer architecture with an attention-based fusion mechanism to model sequence patterns and complex biological contexts. This framework achieves superior predictive performance over existing approaches while providing interpretability analyses that highlight key biological motifs and sequence determinants. Ultimately, this approach serves as a robust tool to facilitate the design of safer and more effective genome-editing strategies.

کلیدواژه ها:

Deep Learning ، CRISPR-Cas۹ ، Off-target prediction ، Bioinformatics