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.
Identifying the relations between chemicals and proteins is an important text mining task. BioCreative VII track 1 DrugProt task aims to promote the development and evaluation of systems that can automatically detect relations between chemical compounds/drugs and genes/proteins in PubMed abstracts. In this paper, we describe our submission, which is an ensemble system, including multiple BERT-based language models. We combine the outputs of individual models using majority voting and multilayer perceptron. Our system obtained 0.7708 in precision and 0.7770 in recall, for an F1 score of 0.7739, demonstrating the effectiveness of using ensembles of BERT-based language models for automatically detecting relations between chemicals and proteins. Our code is available at https://github.com/bionlplab/drugprot_bcvii.
Recently many studies have been conducted on the topic of relation extraction. The DrugProt track at BioCreative VII provides a manually-annotated corpus for the purpose of the development and evaluation of relation extraction systems, in which interactions between chemicals and genes are studied. We describe the ensemble system that we used for our submission, which combines predictions of fine-tuned bioBERT, sciBERT and const-bioBERT models by majority voting. We specifically tested the contribution of syntactic information to relation extraction with BERT. We observed that adding constituentbased syntactic information to BERT improved precision, but decreased recall, since relations rarely seen in the train set were less likely to be predicted by BERT models in which the syntactic information is infused. Our code is available online [https://github.com/Maple177/drugprot-relation-extraction].
In this research, we present our work participation for the DrugProt task of BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowledge base is challenging. To solve this issue, we provide a solution for Track 1, which aims to extract 10 types of interactions between drug and protein entities. We applied an Ensemble Classifier model that combines BioMed-RoBERTa, a state of art language model, with Convolutional Neural Networks (CNN) to extract these relations. Despite the class imbalances in the BioCreative VII DrugProt test corpus, our model achieves a good performance compared to the average of other submissions in the challenge, with the micro F1 score of 55.67% (and 63% on BioCreative VI ChemProt test corpus). The results show the potential of deep learning in extracting various types of DTIs.