  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.
