DrugProt
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BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction
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 archi…
DrugProtRelationRelation ExtractionImproving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models
This paper proposes novel drug-protein relation extraction models that indirectly utilize distant supervision data. Concretely, instead of adding distant supervision data to the manually annotated training data, our mode…
DrugProtRelationRelation ExtractionDoes constituency analysis enhance domain-specific pre-trained BERT models for relation extraction?
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 extr…
DrugProtRelationRelation ExtractionCU-UD: text-mining drug and chemical-protein interactions with ensembles of BERT-based models
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 rel…
DrugProtR-BERT-CNN: Drug-target interactions extraction from biomedical literature
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 …
ArticlesDrug DiscoveryDrugProtLanguage Modeling+1