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Identifying Genomic Data Sources from Biomedical Literature.

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Accurately referencing genomic data in research is crucial for reproducibility and attribution. This study introduces a new corpus and BERT-based models to improve the identification and citation of genomic data sources, enhancing data sharing and reuse.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Science

Background:

  • Genomic research generates vast amounts of data, necessitating robust data citation practices.
  • Current data citation methods are often inadequate, hindering reproducibility and proper attribution in genomic studies.
  • Standardizing data referencing is essential for advancing genomic research and data sharing.

Purpose of the Study:

  • To develop a gold standard corpus for identifying genomic data sources and their attributes.
  • To create and evaluate information extraction models for genomic data mentions and relations.
  • To improve the accuracy and completeness of data citation in genomic research publications.

Main Methods:

  • Creation of a categorized corpus with entities (Dataset, Repository, Contributor) and attributes (Accession Number, URL, DOI).
  • Annotation of relations between data source entities and attributes.
  • Implementation of a BERT-based information extraction pipeline to identify mentions and relationships.
  • Assessment of inter-annotator agreement and model performance using F1 scores.

Main Results:

  • The developed corpus facilitates the identification of genomic data sources and associated metadata.
  • BERT-based models achieved an F1 score of 0.94 for recognizing data source mentions.
  • The models attained an F1 score of 0.76 for extracting relationships between data mentions and attributes.
  • The study demonstrates the effectiveness of the proposed approach in addressing data citation challenges.

Conclusions:

  • The introduced corpus and models represent a significant advancement in the automated recognition of genomic data sources.
  • Improved data citation practices will foster greater data sharing and reuse in genomics.
  • This work provides a foundation for developing more sophisticated tools to manage and cite research data.