Drug protein interaction extraction using scibert based deep learning model
N GabAllah, A Rafea - The International Conference on Innovations in …, 2022 - Springer
The International Conference on Innovations in Computing Research, 2022•Springer
Abstract Information extraction from textual data is becoming more crucial with the increase
of available data on the internet. Automatic extraction of information from biomedical data is
very useful to researchers, saving time and effort exerted by them. Relation extraction
between medical entities is one of the active research areas. In this paper we are presenting
a relation extraction deep learning model based on SciBERT, to extract relations between
drugs/chemicals and proteins/genes entities from PubMed literature. The model could …
of available data on the internet. Automatic extraction of information from biomedical data is
very useful to researchers, saving time and effort exerted by them. Relation extraction
between medical entities is one of the active research areas. In this paper we are presenting
a relation extraction deep learning model based on SciBERT, to extract relations between
drugs/chemicals and proteins/genes entities from PubMed literature. The model could …
Abstract
Information extraction from textual data is becoming more crucial with the increase of available data on the internet. Automatic extraction of information from biomedical data is very useful to researchers, saving time and effort exerted by them. Relation extraction between medical entities is one of the active research areas. In this paper we are presenting a relation extraction deep learning model based on SciBERT, to extract relations between drugs/chemicals and proteins/genes entities from PubMed literature. The model could achieve an average micro F1 score of 91.75% on the ChemProt test set.
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