R-BERT-CNN: Drug-target interactions extraction from biomedical literature

Show full item record



Permalink

http://hdl.handle.net/10138/341302

Citation

Aldahdooh , J , Tanoli , Z & Tang , J 2021 , R-BERT-CNN: Drug-target interactions extraction from biomedical literature . in Proceedings of the BioCreative VII Challenge Evaluation Workshop . pp. 102-106 , BioCreative VII challenge and workshop , 08/11/2021 . < https://biocreative.bioinformatics.udel.edu/resources/publications/bc-vii-workshop-proceedings >

Title: R-BERT-CNN: Drug-target interactions extraction from biomedical literature
Author: Aldahdooh, Jehad; Tanoli, Ziaurrehman; Tang, Jing
Contributor organization: Research Program in Systems Oncology
Department of Mathematics and Statistics
Date: 2021-11-02
Language: eng
Number of pages: 5
Belongs to series: Proceedings of the BioCreative VII Challenge Evaluation Workshop
ISBN: 978-0-578-32368-8
URI: http://hdl.handle.net/10138/341302
Abstract: 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.
Subject: 3111 Biomedicine
Drug-target interaction
Drug discovery
relation extraction
text mining
Peer reviewed: Yes
Rights: cc_by_nc_sa
Usage restriction: openAccess
Self-archived version: publishedVersion


Files in this item

Total number of downloads: Loading...

Files Size Format View
Track1_pos_20_BC7_submission_206.pdf 294.6Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record