# Merged ChemProt-DrugProt for Relation Extraction from Biomedical Literature

arXiv: https://arxiv.org/abs/2405.18605 · HTML: https://mlsanity.com/paper/2405.18605/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Double Graph Based Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2009.13752/) (2020)
- [Recurrent Neural Networks (RNNs): A gentle Introduction and Overview](https://mlsanity.com/paper/1912.05911/) (2019)
- [Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/2112.07869/) (2021)
- [Structured information extraction from complex scientific text with fine-tuned large language models](https://mlsanity.com/paper/2212.05238/) (2022)
- [Improving Biomedical Pretrained Language Models with Knowledge](https://mlsanity.com/paper/2104.10344/) (2021)
- [BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer](https://mlsanity.com/paper/2101.04158/) (2021)
- [Relation extraction from clinical texts using domain invariant convolutional neural network](https://mlsanity.com/paper/1606.09370/) (2016)
- [Extracting Adverse Drug Events from Clinical Notes](https://mlsanity.com/paper/2104.10791/) (2021)
- [Named entity recognition in chemical patents using ensemble of contextual language models](https://mlsanity.com/paper/2007.12569/) (2020)
- [Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach](https://mlsanity.com/paper/2311.06364/) (2023)

## Built on by (most cited first, 1 of 1)

- [Knowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction](https://mlsanity.com/paper/2505.00814/) (2025)

## Abstract (arXiv)

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.