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September 18, 2025Frontiers in GeneticsOpen Access

BioSemAF-BiLSTM: a protein sequence feature extraction framework based on semantic and evolutionary information

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Computational model improves S-sulfenylation site prediction, demonstrating enhanced sensitivity and specificity.

The model achieved an accuracy of 89.32% on an independent test dataset, indicating its strong predictive capability.
Utilizing deep learning with position-specific scoring matrices and subword embeddings, the framework captured essential protein features.
The integration of evolutionary and semantic features effectively improved the identification of S-sulfenylation sites in proteins.
This computational approach highlights the synergy between bioinformatics and deep learning, potentially transforming site prediction.

Zhang et al. (2025) studied this question.

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