计算机科学
关系抽取
关系(数据库)
外部数据表示
背景(考古学)
信息抽取
源代码
代表(政治)
机器学习
数据挖掘
过程(计算)
人工智能
古生物学
生物
政治
政治学
法学
操作系统
作者
Gilchan Park,Sean McCorkle,Carlos Soto,Ian K. Blaby,Shinjae Yoo
标识
DOI:10.1109/bigdata55660.2022.10021099
摘要
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
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