Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention
This addresses the challenge of identifying protein interactions for biological research, offering a novel method that improves over previous approaches without handcrafted features.
The paper tackled the problem of extracting protein-protein interactions from raw text by proposing a tree recurrent neural network with structured attention, achieving state-of-the-art results in precision, recall, and F1-score on AIMed and BioInfer benchmark datasets.
Identifying interactions between proteins is important to understand underlying biological processes. Extracting a protein-protein interaction (PPI) from the raw text is often very difficult. Previous supervised learning methods have used handcrafted features on human-annotated data sets. In this paper, we propose a novel tree recurrent neural network with structured attention architecture for doing PPI. Our architecture achieves state of the art results (precision, recall, and F1-score) on the AIMed and BioInfer benchmark data sets. Moreover, our models achieve a significant improvement over previous best models without any explicit feature extraction. Our experimental results show that traditional recurrent networks have inferior performance compared to tree recurrent networks for the supervised PPI problem.