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Updated: Jul 25, 2025

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ESPERANTO: a GLP-field sEmi-SuPERvised toxicogenomics metadAta curatioN TOol.

Emanuele Di Lieto1, Angela Serra1,2, Simo Iisakki Inkala1

  • 1FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere 33520, Finland.

Bioinformatics (Oxford, England)
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Summary

ESPERANTO is a new framework that improves the FAIRness of toxicogenomics data. It offers standardized, semi-supervised harmonization and integration of metadata, making research data more accessible and reusable.

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Area of Science:

  • Toxicogenomics
  • Bioinformatics
  • Data Science

Background:

  • Biological data repositories are crucial for research evidence.
  • Lack of standardized metadata strategies reduces data FAIRness (Findable, Accessible, Interoperable, Reusable).
  • Data curation for integration is time-consuming, labor-intensive, and error-prone.

Purpose of the Study:

  • To present ESPERANTO, an innovative framework for toxicogenomics metadata harmonization and integration.
  • To enhance the FAIRness of toxicogenomics data.
  • To provide a Good Laboratory Practice-compliant solution for data management.

Main Methods:

  • Development of the ESPERANTO framework.
  • Implementation of a standardized, semi-supervised approach for metadata harmonization.
  • Creation of an ad hoc vocabulary for consistent metadata annotation.
  • Design of a user-friendly interface to support metadata harmonization.

Main Results:

  • ESPERANTO enables standardized harmonization and integration of toxicogenomics metadata.
  • The framework increases the FAIRness of biological data.
  • The tool supports users with varying expertise in metadata harmonization.

Conclusions:

  • ESPERANTO offers a robust solution for improving toxicogenomics data quality and usability.
  • The framework facilitates better data integration and insight generation.
  • ESPERANTO promotes FAIR data principles in biological research.