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Area of Science:

  • Computational biology and bioinformatics
  • Medical informatics and data science

Background:

  • The rapid growth of cancer research data necessitates advanced classification methods beyond journal-level analysis.
  • Existing single-label classification systems inadequately capture the interdisciplinary nature of cancer research literature.
  • A high-resolution, multilabel classifier is needed to improve literature retrieval and clinical relevance screening.

Purpose of the Study:

  • To develop a high-resolution multilabel classifier for cancer research publications.
  • To improve the accuracy and comprehensiveness of literature retrieval for clinical and research purposes.
  • To address the limitations of current journal-level classification systems.

Main Methods:

  • A corpus of 70,599 cancer publications was utilized, divided into training (70%) and testing (30%) sets.
  • A multilabel classifier was developed using the Bidirectional Encoder Representation from Transformers (BERT) + X model architecture.
  • Performance was evaluated by comparing BERT combined with five classical deep learning models (e.g., TextRNN, FastText) using International Cancer Research Partnership terminology.

Main Results:

  • The optimal model combination identified was "BERT + TextRNN", achieving 93.09% precision, 87.75% recall, and 90.34% F1-score.
  • The model demonstrated effective multilabel classification at the publication level.
  • Text structure and multilabel distribution characteristics were quantified for potential generalization to other fields.

Conclusions:

  • The "BERT + TextRNN" model provides high-resolution classification for cancer literature, enhancing retrieval and academic statistics.
  • This model automatically assigns one or more relevant labels to each cancer paper.
  • The "BERT + TextRNN" model outperforms other tested models for multilabel classification of cancer literature, with potential for broader application.