A DNA Sequence Imaging Approach to Predict Splice Sites Using Deep Learning

Kabanga, E.; Jee, S.; Van Messem, A.; De Neve, W.

doi:10.1101/2025.06.24.661415

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bioRxiv · 10.1101/2025.06.24.661415

A DNA Sequence Imaging Approach to Predict Splice Sites Using Deep Learning

Kabanga, E.·Jee, S.·Van Messem, A.·De Neve, W.

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Abstract

We present a novel approach to splice site prediction using image-based deep learning, comparing the established Frequency Chaos Game Representation (FCGR) with our proposed Dinucleotide Fixed Color Pattern (DFCP) technique. Applied to donor and acceptor splice site sequences from Arabidopsis thaliana and Homo sapiens, DFCP consistently outperforms FCGR in accuracy, precision, recall, and F1-score when using a ResNet50 model. Visualization techniques such as saliency maps and Grad-CAM further demonstrate that DFCP produces more localized and biologically interpretable activation patterns. These findings highlight the critical role of sequence visualization strategies in enhancing deep learning performance and interpretability in genomic analysis.

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Kabanga, E., Jee, S., Van Messem, A., De Neve, W.. 2025-06-27. A DNA Sequence Imaging Approach to Predict Splice Sites Using Deep Learning. https://doi.org/10.1101/2025.06.24.661415

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