Abstract：A key challenge in genetic diagnosis is the assessment of pathogenic genetic mutations related to splicing. Existing predictive tools for pathogenic splicing mutations are mostly based on traditional machine learning methods, heavily relying on manually extracted splicing features. Thereby the predictive performance is limited, especially for non-canonical splicing mutation producing poor performance. Therefore, a bidirectional encoder representations from transformers(BERT) and convolutional neural network(CNN)-based deleterious splicing mutation prediction method(BCsplice) is proposed. The BERT module in BCsplice comprehensively extracts contextual information of sequences. While combined with CNN that extracts local features, BERT module can adequately learn the semantic information of sequences and predict the pathogenicity of splicing mutations. The impact of non-canonical splicing mutations often relies more on deep semantic information of sequence context. By combining and extracting the multi-level semantic information of BERT through CNN, rich information representations can be obtained, aiding in the identification of non-canonical splicing mutations. Comparative experiments demonstrate the superior performance of BCsplice, especially exhibiting certain performance advantages in non-canonical splicing regions, and it contributes to the identification of pathogenic splicing mutations and clinical genetic diagnosis.
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