Extracting Protein-Protein Interactions (PPIs) from Biomedical
  Literature using Attention-based Relational Context Information
Gilchan Park Sean McCorkle Carlos Soto Ian Blaby Shinjae Yoo
Because protein-protein interactions (PPIs) are crucial to understand living
systems, harvesting these data is essential to probe disease development and
discern gene/protein functions and biological processes. Some curated datasets
contain PPI data derived from the literature and other sources (e.g., IntAct,
BioGrid, DIP, and HPRD). However, they are far from exhaustive, and their
maintenance is a labor-intensive process. On the other hand, machine learning
methods to automate PPI knowledge extraction from the scientific literature
have been limited by a shortage of appropriate annotated data. This work
presents a unified, multi-source PPI corpora with vetted interaction
definitions augmented by binary interaction type labels and a Transformer-based
deep learning method that exploits entities' relational context information for
relation representation to improve relation classification performance. The
model's performance is evaluated on four widely studied biomedical relation
extraction datasets, as well as this work's target PPI datasets, to observe the
effectiveness of the representation to relation extraction tasks in various
data. Results show the model outperforms prior state-of-the-art models. The
code and data are available at:
https://github.com/BNLNLP/PPI-Relation-Extraction