BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction
<|reference_start|>BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction: This paper presents a methodology for enhancing relation extraction from biomedical texts, focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a multi-layer fully connected network architecture, our approach integrates the ChemProt and DrugProt datasets using a novel merging strategy. Through extensive experimentation, we demonstrate significant performance improvements, particularly in CPR groups shared between the datasets. The findings underscore the importance of dataset merging in augmenting sample counts and improving model accuracy. Moreover, the study highlights the potential of automated information extraction in biomedical research and clinical practice.<|reference_end|>
@article{mcinnes2024biobert-based,
    title={BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced
  Biomedical Relation Extraction},
    author={Bridget T. McInnes, Jiawei Tang, Darshini Mahendran, Mai H. Nguyen},
    journal={arXiv preprint arXiv:2405.18605},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18605},
    primaryClass={cs.CL cs.IR q-bio.MN}
}