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Biomedical relation extraction method based on ensemble learning and attention mechanism.

Yaxun Jia¹, Haoyang Wang², Zhu Yuan³

¹Department of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.

BMC Bioinformatics

|October 18, 2024

Summary

This study introduces SARE, a novel approach for biomedical relation extraction using ensemble learning and attention mechanisms. SARE enhances accuracy and efficiency in extracting complex relationships from biomedical texts.

Keywords:

Attention mechanism BERT Biomedical relation extraction Deep learning Stacking

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Area of Science:

Biomedical Informatics
Computational Biology
Natural Language Processing

Background:

Relation extraction (RE) is vital for biomedical research, identifying semantic links in textual data.
The growing volume of biomedical literature necessitates advanced computational models for large-scale RE.
Accurate and efficient RE is crucial for biomedical informatics advancements.

Purpose of the Study:

To propose a novel approach, SARE, for enhancing biomedical relation extraction.
To improve the accuracy and efficiency of extracting complex semantic relationships from biomedical texts.
To leverage ensemble learning and attention mechanisms for robust RE.

Main Methods:

SARE combines ensemble learning (Stacking) with attention mechanisms.
Utilizes multiple pre-trained models for improved adaptability and robustness.
Attention mechanisms focus on capturing and utilizing key textual information.

Main Results:

SARE demonstrated performance improvements of 4.8, 8.7, and 0.8 percentage points on PPI, DDI, and ChemProt datasets.
Outperformed original BERT and PubMedBERT models in biomedical relation extraction tasks.
Showcased enhanced adaptability and robustness across diverse biomedical domains.

Conclusions:

SARE presents a promising solution for accurate and efficient biomedical relation extraction.
Combining ensemble learning with attention mechanisms effectively extracts complex relationships.
Publicly available code and data facilitate further research and application.