用Transformer预测蛋白质二级结构，提升长距离残基关系捕捉能力
Protein Secondary Structure Prediction Using Transformers
- 基于注意力机制的Transformer模型处理蛋白序列
- 在CB513数据集上通过滑动窗口增强训练样本
- 能有效建模局部与远距离残基相互作用
从氨基酸序列预测蛋白质二级结构（如α螺旋、β折叠和无规卷曲）对理解蛋白质功能至关重要。本文提出一种基于Transformer的模型，利用注意力机制处理蛋白序列数据以预测结构基元。在CB513数据集上采用滑动窗口数据增强技术扩展训练样本。该模型展现出对不同长度序列的强大泛化能力，并有效捕捉残基间的局部与长程相互作用。
原文摘要 · Abstract (English)
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.
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