Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection

TL;DR

This paper introduces a Knowledge Selection Model (KSM) that enhances neural protein-protein interaction extraction by selectively integrating prior knowledge with context, achieving state-of-the-art results on a benchmark dataset.

Contribution

The paper proposes a novel KSM that effectively fuses selected prior knowledge with context for improved PPI extraction, addressing the challenge of knowledge relevance.

Findings

KSM achieves a 38.08% F1-score on BioCreative VI PPI dataset.

Knowledge selection improves PPI extraction performance.

The model outperforms previous methods on the benchmark dataset.

Abstract

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.