CRISPR-Embedding: CRISPR/Cas9 off-target activity prediction using DNA k-mer embedding

CRISPR/Cas9 has revolutionized gene editing, offering biologists and researchers a powerful tool for targeted genome modification. Despite its promise, the technique carries the risk of unintended off-target edits, which can disrupt normal cellular functions. To enhance the accuracy of off-target predictions, various computational approaches have been explored; however, traditional methods often face challenges such as data imbalance and high model complexity. In this paper, we present CRISPR-Embedding, a deep learning model based on a 9-layer Convolutional Neural Network (CNN) designed to predict CRISPR/Cas9 off-target activity. Our model utilizes DNA k -mer embeddings for effective sequence representation. To address the issue of data imbalance, we applied data augmentation and under-sampling strategies, resulting in a cleaner, more balanced dataset. Through 5-fold cross-validation,…