{"ID":2831210,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08613","arxiv_id":"2512.08613","title":"Protein Secondary Structure Prediction Using Transformers","abstract":"Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data augmentation technique is used on the CB513 dataset to expand the training samples. The transformer shows strong ability to generalize across variable-length sequences while effectively capturing both local and long-range residue interactions.","short_abstract":"Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data aug...","url_abs":"https://arxiv.org/abs/2512.08613","url_pdf":"https://arxiv.org/pdf/2512.08613v1","authors":"[\"Manzi Kevin Maxime\"]","published":"2025-12-09T13:58:47Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}