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Biomedical Relation Extraction Using Dependency Graph and Decoder-Enhanced Transformer Model.

Seonho Kim1, Juntae Yoon2, Ohyoung Kwon3

  • 1Department of Computer Science and Engineering, Sogang University, Seoul 04107, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces T5_slim_dec, a novel model for extracting drug-drug and chemical-protein interactions. T5_slim_dec achieved high accuracy on benchmark datasets, demonstrating the effectiveness of tailored transformer architectures for biomedical relation extraction.

Keywords:
CPR (chemical–protein relation)ChemProtDDI (drug–drug interaction)GAT (graph-attention network)T5 (text-to-text transfer transformer)relation extractionself-attentiontransformer

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Accurate identification of drug-drug interactions (DDIs) and chemical-protein interactions (CPIs) is crucial for drug development and understanding disease mechanisms.
  • Existing methods often struggle to capture complex relational information within biomedical texts.
  • Transfer learning with transformer models offers a promising avenue for improving relation extraction tasks.

Purpose of the Study:

  • To evaluate and enhance the performance of transformer-based models for extracting drug-drug and chemical-protein interactions.
  • To investigate the impact of incorporating structural information (syntactic and graph-based) on relation extraction.
  • To propose novel model architectures, specifically T5_slim_dec and BERT_GAT, for biomedical relation extraction.

Main Methods:

  • Utilized transfer transformers, including BERT_GAT and T5, to extract interactions from the DDI Extraction-2013 and BioCreative ChemProt datasets.
  • Proposed BERT_GAT, incorporating a graph attention network (GAT) to leverage local sentence structure and node embeddings.
  • Developed T5_slim_dec by adapting the T5 model's autoregressive generation for relation classification, removing the self-attention layer in the decoder.

Main Results:

  • T5_slim_dec achieved high accuracy, reaching 91.15% on the DDI dataset and 94.29% for the Chemical-Protein Relation (CPR) class group in the ChemProt dataset.
  • BERT_GAT did not show significant performance improvements compared to baseline transformer models for relation extraction.
  • Demonstrated that transformer models focusing on word relationships can implicitly understand language without explicit structural information.

Conclusions:

  • The T5_slim_dec model, with its specialized decoder for classification, shows significant promise for biomedical relation extraction tasks.
  • Transformer-based approaches are effective for understanding language in the context of biomedical relationships.
  • Further research into tailored transformer architectures can lead to improved accuracy in identifying critical drug and chemical interactions.