Computational Biology and Chemistry, 2019, 83: 107146
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.
Huiwei Zhou, Xuefei Li, Weihong Yao, Zhuang Liu, Shixian Ning, Chengkun Lang, Lei Du