Biomedical Relation Extraction

Biomedical relation extraction (RE) aims to automatically identify and classify relationships between entities (e.g., genes, diseases, chemicals) mentioned in biomedical texts, facilitating knowledge discovery and database construction. Current research heavily utilizes deep learning models, particularly transformer-based architectures like BioBERT and other pre-trained language models, often enhanced by techniques such as natural language inference (NLI) and data augmentation strategies like dataset merging and instruction finetuning. The improved accuracy and efficiency of biomedical RE significantly impacts downstream applications, including literature-based drug discovery, clinical decision support, and the creation of comprehensive biomedical knowledge graphs.