UNIGE document Chapitre d'actes
previous document  unige:162727  next document
add to browser collection
Title

Drug-protein relation extraction using ensemble of transformer-based language models

Authors
Published in Proceedings of the BioCreative VII Challenge Evaluation Workshop. Online - November 8-10, 2021 - . 2021, p. 89-93
Abstract Drug-protein interactions have become a crucial component to study potential side effects, discover new uses for existing drugs, to name a few applications. We describe our approach based on transformer-based language models to predict relations between chemical and gene entities in DrugProt corpus. Sliding window is used to detect the relation in a passage for the individual models, and then they are combined using majority vote. Our model achieved 60% of F1-score (88% of recall and 45% of precision) in the track 1: text mining drug and chemical- protein interactions at BioCreative VII. Ensemble of transformer-based language models provides a baseline performance for drug-protein interaction extraction
Keywords TransformersRelation extractionEnsembleBERT
Identifiers
ISBN: 978-0-578-32368-8
Full text
Structures
Research group DS4DH - Data Science for Digital Health (1035)
Citation
(ISO format)
COPARA ZEA, Jenny Linet, TEODORO, Douglas. Drug-protein relation extraction using ensemble of transformer-based language models. In: Proceedings of the BioCreative VII Challenge Evaluation Workshop. Online. [s.l.] : [s.n.], 2021. p. 89-93. https://archive-ouverte.unige.ch/unige:162727

49 hits

12 downloads

Update

Deposited on : 2022-08-19

Export document
Format :
Citation style :