Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory

Yu Lun Hsieh, Yung-Chun Chang, Nai Wen Chang, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議貢獻

摘要

Accurate identification of protein-protein interaction (PPI) helps biomedical researchers to quickly capture crucial information in literatures. This work proposes a recurrent neural network (RNN) model to identify PPIs. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that RNN outperforms state-of-the-art methods with relative improvements of 10% and 18%, respectively. Cross-corpus evaluation also indicates that RNN is robust even when trained on data from different domains. These results suggest that RNN effectively captures semantic relationships among proteins without any feature engineering.
原文英語
主出版物標題Proceedings of the Eighth International Joint Conference on Natural Language Processing
發行者Asian Federation of Natural Language Processing
頁面240-245
出版狀態已發佈 - 2017

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  • 引用此

    Hsieh, Y. L., Chang, Y-C., Chang, N. W., & Hsu, W. L. (2017). Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory. 於 Proceedings of the Eighth International Joint Conference on Natural Language Processing (頁 240-245). Asian Federation of Natural Language Processing.