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RENET2: high-performance full-text gene-disease relation extraction with iterative training data expansion.

Junhao Su1, Ye Wu1, Hing-Fung Ting1

  • 1Department of Computer Science, The University of Hong Kong, Hong Kong, 999077, China.

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Summary

RENET2 efficiently extracts gene-disease associations from full biomedical texts. This deep learning method significantly outperforms existing tools, enabling large-scale discovery of crucial biological relationships.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Relation extraction (RE) is vital for identifying gene-disease associations in biomedical literature.
  • Current RE tools often struggle with full-text articles, limiting comprehensive data extraction.
  • Extracting gene-disease links from full-text is challenging due to data volume and complexity.

Purpose of the Study:

  • To develop RENET2, a deep learning model for extracting gene-disease associations from full-text biomedical articles.
  • To address the scarcity of annotated full-text data using an iterative training data expansion strategy.
  • To improve the accuracy and efficiency of gene-disease association extraction from extensive scientific literature.

Main Methods:

  • Developed RENET2, a deep learning-based relation extraction method.
  • Implemented Section Filtering and ambiguous relation modeling for enhanced extraction.
  • Created a novel iterative training data expansion strategy for full-text annotation.

Main Results:

  • RENET2 achieved an F1-score of 72.13% on an annotated full-text dataset.
  • Demonstrated significant performance gains over existing methods (BeFree, DTMiner, BioBERT, RENET).
  • Identified approximately 3.72 million gene-disease associations from ~1.89 million PubMed Central articles.

Conclusions:

  • RENET2 is an efficient and accurate tool for full-text gene-disease association extraction.
  • The method facilitates large-scale biomedical data mining and discovery.
  • The developed dataset and source code are publicly available for research use.