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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Biomedical Terminology Mapper for UML projects.

Julien C Thibault1, Lewis Frey

  • 1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.

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|December 5, 2013
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Summary
This summary is machine-generated.

This study introduces a novel algorithm to map local biomedical data implementations to standard terminologies, improving data exchange and interoperability. The automated approach shows promising results for accelerating the mapping process.

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Area of Science:

  • Biomedical Informatics
  • Data Standards
  • Software Engineering

Background:

  • The increasing volume of biomedical data necessitates standardized descriptions for effective exchange and interoperability.
  • Current methods for mapping local data implementations to standard terminologies are often manual and time-consuming.

Purpose of the Study:

  • To present a novel algorithm for automatically mapping Unified Modeling Language (UML) class and attribute names to standard biomedical terminologies.
  • To evaluate the algorithm's performance using real-world data from caGrid.

Main Methods:

  • The algorithm normalizes and tokenizes UML names.
  • It then performs lookups in a Unified Medical Language System (UMLS)-based dictionary.
  • 142 UML projects from caGrid were mapped to National Cancer Institute (NCI) terminology concepts for evaluation.

Main Results:

  • The algorithm successfully mapped UML class and attribute names to NCI terminology concepts.
  • Mappings were compared against manually curated annotations in caGrid.
  • The automated mapping results were promising, indicating potential for significant time savings.

Conclusions:

  • The developed algorithm offers a promising solution for automating the mapping of local implementations to standard biomedical terminologies.
  • This approach can significantly expedite the process, enhancing data interoperability in the biomedical community.