Merged ChemProt-DrugProt for Relation Extraction from Biomedical Literature

TL;DR

This paper merges ChemProt and DrugProt datasets to enhance relation extraction between chemicals and genes, demonstrating improved model performance by combining BioBERT with GCNs for better global and local context understanding.

Contribution

It introduces a merged dataset from ChemProt and DrugProt and evaluates the benefits of integrating GCNs with BioBERT for relation extraction.

Findings

Merged datasets improve model accuracy in shared CPR groups.

Integrating GCNs with BioBERT enhances global context understanding.

Significant performance gains in precision and recall for certain relation groups.

Abstract

The extraction of chemical-gene relations plays a pivotal role in understanding the intricate interactions between chemical compounds and genes, with significant implications for drug discovery, disease understanding, and biomedical research. This paper presents a data set created by merging the ChemProt and DrugProt datasets to augment sample counts and improve model accuracy. We evaluate the merged dataset using two state of the art relationship extraction algorithms: Bidirectional Encoder Representations from Transformers (BERT) specifically BioBERT, and Graph Convolutional Networks (GCNs) combined with BioBERT. While BioBERT excels at capturing local contexts, it may benefit from incorporating global information essential for understanding chemical-gene interactions. This can be achieved by integrating GCNs with BioBERT to harness both global and local context. Our results show that by integrating the ChemProt and DrugProt datasets, we demonstrated significant improvements in model performance, particularly in CPR groups shared between the datasets. Incorporating the global context using GCN can help increase the overall precision and recall in some of the CPR groups over using just BioBERT.