Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information
This work addresses the labor-intensive and incomplete nature of existing PPI databases by automating extraction from literature, which is crucial for biomedical researchers studying diseases and biological processes.
The authors tackled the problem of extracting protein-protein interactions (PPIs) from biomedical literature by creating a unified multi-source PPI dataset and developing a Transformer-based method that uses relational context information, resulting in a model that outperforms prior state-of-the-art models on multiple datasets.
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