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BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets.

Po-Ting Lai¹, Chih-Hsuan Wei¹, Ling Luo²

¹National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894 Bethesda, USA.

Journal of Biomedical Informatics

|September 6, 2023

Summary

This study introduces BioREx, a novel framework and data-centric approach that significantly improves biomedical relation extraction by combining diverse datasets. BioREx achieves state-of-the-art performance and enhances model generalizability for various biomedical NLP tasks.

Keywords:

Biomedical dataset Biomedical natural language processing Multi-task learning Transfer learning Transformers

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Area of Science:

Biomedical Natural Language Processing (NLP)
Bioinformatics
Computational Biology

Background:

Biomedical relation extraction (RE) identifies relationships between biomedical entities in text.
Current RE methods struggle with small, domain-specific datasets due to costly manual annotation.
This limits the development of generalized and high-performing RE models.

Purpose of the Study:

To present a novel framework for systematically addressing data heterogeneity in biomedical RE datasets.
To develop BioREx, a data-centric approach for relation extraction using a combined, large-scale dataset.
To improve the performance and generalizability of biomedical RE models.

Main Methods:

Developed a framework to integrate heterogeneous biomedical RE datasets into a unified, large-scale dataset.
Implemented BioREx, a data-centric method leveraging the combined dataset for relation extraction.
Evaluated BioREx against benchmark systems and state-of-the-art methods like transfer and multi-task learning.

Main Results:

BioREx achieved a new state-of-the-art F1-score of 79.6% on the BioRED corpus, surpassing the benchmark by 5.2%.
The combined dataset improved performance across five different RE tasks.
BioREx demonstrated favorable performance compared to transfer learning and multi-task learning, and showed robustness on unseen tasks.

Conclusions:

The proposed framework and BioREx effectively address data limitations in biomedical RE.
BioREx offers a robust and generalizable solution, advancing the field of biomedical NLP.
The integrated dataset and BioREx tool are publicly available to facilitate further research.