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.
|Title of host publication||Proceedings of the Eighth International Joint Conference on Natural Language Processing|
|Publisher||Asian Federation of Natural Language Processing|
|Publication status||Published - 2017|
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. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (pp. 240-245). Asian Federation of Natural Language Processing.