BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction

Bridget T. McInnes, Jiawei Tang, Darshini Mahendran, Mai H. Nguyen
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Abstract

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.
基于 BioBERT 的深度学习和合并 ChemProt-DrugProt 用于增强生物医学关系提取
我们的方法利用 BioBERT 模型和多层全连接网络架构,采用新颖的合并策略整合了 ChemProt 和 DrugProt 数据集。通过广泛的实验,我们证明了性能的显著提高,尤其是在数据集之间共享的 CPR 组中。这些发现强调了数据集合并在增加样本数量和提高模型准确性方面的重要性。此外,这项研究还凸显了自动信息提取在生物医学研究和临床实践中的潜力。
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