Similarity-based transfer learning with deep learning networks for accurate CRISPR-Cas9 off-target prediction.

ABSTRACT: Transfer learning has emerged as a powerful tool for enhancing predictive accuracy in complex tasks, particularly in scenarios where data is limited or imbalanced. This study explores the use of similarity-based pre-evaluation as a methodology to identify optimal source datasets for transfer learning, addressing the dual challenge of efficient source-target dataset pairing and off-target prediction in CRISPR-Cas9, while existing transfer learning applications in the field of gene editing often lack a principled method for source dataset selection. We use cosine, Euclidean, and Manhattan distances to evaluate similarity between the source and target datasets used in our transfer learning experiments. Four deep learning network architectures, i.e. Multilayer Perceptron (MLP), Convolutional N

SUBMITTER: Charlier J 

PROVIDER: S-EPMC12571277 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature