Author summary CRISPR-Cas9 is a popular gene-editing technology that allows researchers to modify an organism’s genomic DNA at precise locations. Significant research efforts have been focusing on improving its precision and effectiveness, with particular emphasis on minimizing off-target effects. At the same time, transfer learning techniques are becoming increasingly important for addressing deep learning challenges in computational biology, especially in the field of CRISPR-Cas9, where plausible training data availability can be limited. This study investigates the effectiveness of integrating similarity-based analysis with transfer learning for improving CRISPR-Cas9 off-target prediction. Our key contribution consists in an experimental evaluation of three distance metrics, i.e. cosine, Euclidean, and Manhattan distances, along with several traditional machine learning and deep learning models, in the context of knowledge transfer by transfer learning applied to gene editing data. For each considered target dataset our transfer learning framework determines the most suitable source dataset to be used in the model pre-training. The proposed computational framework offers a reliable and systematic method for selecting suitable source data, streamlining the transfer learning process, and improving prediction accuracy.
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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 simila… [65184 chars]
Source: PLOS (Public Library of Science) | Published: 2025-10-24T00:00:00Z
Credit: PLOS (Public Library of Science)
