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This study introduces a tool for normalizing unstructured biomedical text data, improving its usability for automated analysis. The method standardizes free text, enhancing data findability and integration with ontologies.

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Natural Language Processing

Background:

  • Unstructured biomedical data, like free text, is underutilized due to extraction challenges.
  • Normalizing free text allows for structured representation using ontologies and harmonized queries.
  • This paper presents a tool for free-text normalization applied to Immune Epitope Database (IEDB) data.

Purpose of the Study:

  • To develop an adaptable tool for normalizing free-text biomedical data.
  • To evaluate the tool's effectiveness on age and data-location fields from the IEDB.
  • To enhance the usability and findability of unstructured biomedical data.

Main Methods:

  • Free text entries for age and data-location from the IEDB were analyzed.
  • Normalization involved three steps: character, word, and phrase normalization.
  • Generalizable rules were developed and applied using the presented tool.

Main Results:

  • For age data, output validity reached 99.97% (character), 98.06% (word), and 83.81% (phrase).
  • For data-location data, output validity reached 99.99% (character), 98.46% (word), and 97.95% (phrase).
  • The normalization process significantly reduced data variance.

Conclusions:

  • A generalizable approach for normalizing free-text database fields was developed.
  • The one-time effort of rule creation can be applied to ongoing data curation.
  • Standardization enhances data findability, usability, search functionality, and ontology linkages.