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Enhancing the coverage of SemRep using a relation classification approach.

Shufan Ming1, Rui Zhang2, Halil Kilicoglu1

  • 1School of Information Sciences, University of Illinois Urbana-Champaign, 501 E Daniel St., Champaign, 61820, IL, USA.

Journal of Biomedical Informatics
|May 23, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances biomedical relation extraction by improving recall, significantly increasing the size and utility of the Semantic MEDLINE Database (SemMedDB). The new model doubles SemMedDB

Keywords:
Biomedical relation extractionLarge language modelsRelation classificationSemMedDBSemRep

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Relation extraction from biomedical literature is crucial for applications like drug repurposing.
  • The SemRep tool extracts subject-predicate-object triples but has low recall (0.42).
  • The Semantic MEDLINE Database (SemMedDB) relies on SemRep for its data.

Purpose of the Study:

  • To enhance SemRep's performance using a relation classification approach.
  • To increase the size and utility of the SemMedDB by improving relation extraction recall.

Main Methods:

  • Developed a relation classification model leveraging PubMedBERT with contrastive pre-training.
  • Experimented with mention, semantic type, and semantic group entity representations.
  • Evaluated performance on the SemRep Gold Standard dataset and 12K PubMed abstracts.

Main Results:

  • The best model achieved precision of 0.62, recall of 0.81, and an F1 score of 0.70.
  • The model has the potential to double the size of SemMedDB when applied to all of PubMed.
  • Manual assessment confirmed 67% accuracy for novel triples identified by the model.

Conclusions:

  • The developed model shows significant promise for more comprehensive biomedical relation extraction.
  • This advancement can enhance downstream applications in biomedical literature mining.
  • The study provides data and code for reproducibility and further development.