# BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets

arXiv: https://arxiv.org/abs/2306.11189 · HTML: https://mlsanity.com/paper/2306.11189/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling](https://mlsanity.com/paper/2010.11304/) (2020)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022)
- [HIN: Hierarchical Inference Network for Document-Level Relation Extraction](https://mlsanity.com/paper/2003.12754/) (2020)
- [An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining](https://mlsanity.com/paper/2005.02799/) (2020)
- [A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization](https://mlsanity.com/paper/1812.06081/) (2018)
- [A Dataset for N-ary Relation Extraction of Drug Combinations](https://mlsanity.com/paper/2205.02289/) (2022)
- [Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework](https://mlsanity.com/paper/2009.09509/) (2020)

## Built on by (most cited first, 8 of 8)

- [PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge](https://mlsanity.com/paper/2401.11048/) (2024)
- [Enhancing Biomedical Relation Extraction with Directionality](https://mlsanity.com/paper/2501.14079/) (2025)
- [Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG](https://mlsanity.com/paper/2604.16422/) (2026)
- [EMBRE: Entity-aware Masking for Biomedical Relation Extraction](https://mlsanity.com/paper/2401.07877/) (2024)
- [EnzChemRED, a rich enzyme chemistry relation extraction dataset](https://mlsanity.com/paper/2404.14209/) (2024)
- [A Library Perspective on Supervised Text Processing in Digital Libraries: An Investigation in the Biomedical Domain](https://mlsanity.com/paper/2411.12752/) (2024)
- [Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease](https://mlsanity.com/paper/2603.15711/) (2026)
- [UniD3: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning](https://mlsanity.com/paper/2606.01394/) (2026)

## Abstract (arXiv)

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.