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Updated: Jan 16, 2026

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生物SemAF-BiLSTM:基于语义和进化信息的蛋白质序列特征提取框架.

Zihan Zhang1, Yixuan Wang2

  • 1Haide College, Ocean University of China, Qingdao, China.

Frontiers in genetics
|October 3, 2025
PubMed
概括

一个新的深度学习模型,BioSemAF-BiLSTM,准确地预测S-硫化位点. 这种计算工具有助于理解蛋白质功能和疾病相关性,克服实验的局限性.

关键词:
科尔摩戈罗夫的复杂性是这样的.适应性融合 适应性融合双向的LSTM神经网络是一个双向的神经网络.生物信息编码的编码.信息丢失是信息丢失.后翻译修改后的修改嵌入蛋白质序列嵌入蛋白质序列硫化场所的硫化场所

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