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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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BioBERT and Similar Approaches for Relation Extraction.

Balu Bhasuran1,2

  • 1DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore, Tamilnadu, India. balubhasuran08@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
|June 17, 2022
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Summary

This study details a protocol for biomedical relation extraction using BERT, a deep learning model. It adapts BERT for the biomedical domain, enhancing information extraction from scientific literature.

Keywords:
BERTBioBERTDeep LearningRelation ExtractionText MiningTransformers

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Deep Learning

Background:

  • Biomedical research contains vast relational data across millions of publications.
  • Extracting this information is crucial for drug repurposing and precision medicine.
  • General domain language models like BERT perform poorly on specialized biomedical text.

Purpose of the Study:

  • To present a protocol for biomedical relation extraction using BERT.
  • To adapt BERT models for the specific needs of the biomedical domain.
  • To improve information extraction from scientific literature.

Main Methods:

  • Utilizing Bidirectional Encoder Representations from Transformers (BERT) architecture.
  • Adapting and pretraining BERT on a large corpus of biomedical literature (PubMed, PubMed Central).
  • Fine-tuning BERT models, specifically BioBERT, for relation extraction tasks.
  • Incorporating knowledge graph infusion into the BERT model layers.

Main Results:

  • Demonstrates a protocol for effective biomedical relation extraction.
  • Highlights the importance of domain-specific pretraining for BERT models.
  • Shows improved performance of adapted BERT versions like BioBERT in biomedical NLP tasks.

Conclusions:

  • Domain-specific BERT models significantly enhance information extraction from biomedical literature.
  • The proposed protocol offers a framework for leveraging deep learning in biomedical text mining.
  • Further advancements can be achieved through knowledge graph integration with BERT.