BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets

• Published in: Journal of biomedical informatics Vol. 146; p. 104487
• Main Authors: Lai, Po-Ting, Wei, Chih-Hsuan, Luo, Ling, Chen, Qingyu, Lu, Zhiyong
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2023
• Subjects: Biomedical dataset; Biomedical natural language processing; Multi-task learning; Transfer learning; Transformers; Biomedical natural language processing; Transformers; Transfer learning; Multi-task learning; Biomedical dataset
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2023.104487

[Display omitted] Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomedical natural language processing (NLP) research and plays a critical role in many downstream applications, such as literature-based discovery and knowledge graph construction. State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein–protein interaction and chemical-induced disease relation. Manual dataset annotation, however, is highly expensive and time-consuming, as it requires domain knowledge. Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. Based on the framework and dataset, we report on BioREx, a data-centric approach for extracting relations. Our evaluation shows that BioREx achieves significantly higher performance than the benchmark system trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in F-1 measure on the recently released BioRED corpus. We further demonstrate that the combined dataset can improve performance for five different RE tasks. In addition, we show that on average BioREx compares favorably to current best-performing methods such as transfer learning and multi-task learning. Finally, we demonstrate BioREx’s robustness and generalizability in two independent RE tasks not previously seen in training data: drug-drug N-ary combination and document-level gene-disease RE. The integrated dataset and optimized method have been packaged as a stand-alone tool available at https://github.com/ncbi/BioREx.