A sequence labeling framework for extracting drug –protein relations from biomedical literature

AbstractAutomatic extracting interactions between chemical compound/drug and gene/protein are significantly beneficial to drug discovery, drug repurposing, drug design and biomedical knowledge graph construction. To promote the development of the relation extraction between drug and protein, the BioCreative VII challenge organized the DrugProt track. This paper describes the approach we developed for this task. In addition to the conventional text classification framework that has been widely used in relation extraction tasks, we propose a sequence labeling framework to drug –protein relation extraction. We first comprehensively compared the cutting-edge biomedical pre-trained language models for both frameworks. Then, we explored several ensemble methods to further improve the final performance. In the evaluation of the challenge, our best submission (i.e. the ensemb le of models in two frameworks via major voting) achieved the F1-score of 0.795 on the official test set. Further, we realized the sequence labeling framework is more efficient and achieves better performance than the text classification framework. Finally, our ensemble of the sequence labeling mode ls with majority voting achieves the best F1-score of 0.800 on the test set.Database URLhttps://github.com/lingluodlut/BioCreativeVII_DrugProt
Source: Database : The Journal of Biological Databases and Curation - Category: Databases & Libraries Source Type: research