Combining GCN and Bi-LSTM for Protein Secondary Structure Prediction
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(2021)
摘要
Protein secondary structure prediction is still a challenging task in bioinformatics, especially for 8-state (Q8) classification. To address this problem, we have proposed a deep learning based model by integrating graph convolutional network(GCN) and bidirectional long short-term memory (Bi-LSTM) network in this paper. In the model, GCN is utilized to synthesize the information of amino acids and...
更多查看译文
关键词
Proteins,Deep learning,Protein engineering,Biological system modeling,Predictive models,Amino acids,Encoding
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要