.png){kind=icon}
Return
Biomedical Relation Extraction Using Dependency Graph and Decoder-Enhanced Transformer Model
.png){kind=icon}
OA
AI
DOI：10.3390/bioengineering10050586.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The identification of drug-drug and chemical-protein interactions is essential for understanding unpredictable changes in the pharmacological effects of drugs and mechanisms of diseases and developing therapeutic drugs. In this study, we extract drug-related interactions from the DDI (Drug-Drug Interaction) Extraction-2013 Shared Task dataset and the BioCreative ChemProt (Chemical-Protein) dataset using various transfer transformers. We propose BERTGAT that uses a graph attention network (GAT) to take into account the local structure of sentences and embedding features of nodes under the self-attention scheme and investigate whether incorporating syntactic structure can help relation extraction. In addition, we suggest T5(slim_dec), which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the relation classification problem by removing the self-attention layer in the decoder block. Furthermore, we evaluated the potential of biomedical relation extraction of GPT-3 (Generative Pre-trained Transformer) using GPT-3 variant models. As a result, T5(slim_dec), which is a model with a tailored decoder designed for classification problems within the T5 architecture, demonstrated very promising performances for both tasks. We achieved an accuracy of 91.15% in the DDI dataset and an accuracy of 94.29% for the CPR (Chemical-Protein Relation) class group in ChemProt dataset. However, BERTGAT did not show a significant performance improvement in the aspect of relation extraction. We demonstrated that transformer-based approaches focused only on relationships between words are implicitly eligible to understand language well without additional knowledge such as structural information.
Keywords:
DDI (drug-drug interaction)
CPR (chemical-protein relation)
transformer
self-attention
GAT (graph-attention network)
relation extraction
ChemProt
T5 (text-to-text transfer transformer)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
B
IF:
3.7
Papers:
5.9K
Citations:
1.3W
Organization
Cited Papers
Matrix Effects in the Detection of Pb and Ba in Soils Using Laser-Induced Breakdown Spectroscopy
PREAI
ShareSaveShareSaveShareSaveCopolymerization of fluorinated acrylic monomers with p‐vinylphenol and hydroxyethyl methacrylate
PREAI
ShareSaveShareSaveShareSaveIUPHAR-DB: the IUPHAR database of G protein-coupled receptors and ion channels.png)
NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSave
View more