Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection
This work addresses the challenge of improving precision medicine efforts by enhancing PPI extraction from literature, though it is incremental as it builds on existing methods with knowledge selection.
The paper tackles the problem of extracting protein-protein interactions from scientific literature by selectively using prior knowledge from knowledge bases, achieving a new state-of-the-art F1-score of 38.08% on the BioCreative VI dataset.
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and their relations, which can be encoded in entity and relation embeddings to help PPI extraction. However, the prior knowledge of protein-protein pairs must be selectively used so that it is suitable for different contexts. This paper proposes a Knowledge Selection Model (KSM) to fuse the selected prior knowledge and context information for PPI extraction. Firstly, two Transformers encode the context sequence of a protein pair according to each protein embedding, respectively. Then, the two outputs are fed to a mutual attention to capture the important context features towards the protein pair. Next, the context features are used to distill the relation embedding by a knowledge selector. Finally, the selected relation embedding and the context features are concatenated for PPI extraction. Experiments on the BioCreative VI PPI dataset show that KSM achieves a new state-of-the-art performance (38.08% F1-score) by adding knowledge selection.