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Information Retrieval Using Machine Learning for Biomarker Curation in the Exposome-Explorer.

Andre Lamurias1, Sofia Jesus1, Vanessa Neveu2

  • 1LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.

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Summary

This study introduces a machine learning pipeline to automate literature review for environmental disease risk factor databases. The approach significantly reduces manual screening, improving efficiency for biomarker discovery and data curation.

Keywords:
biomarkers of exposuredatabase curationinformation retrievalmachine learningtext mining

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Area of Science:

  • Environmental Health Sciences
  • Bioinformatics
  • Computational Biology

Background:

  • The Exposome-Explorer database, established in 2016, manually curates biomarkers for environmental disease risk factors.
  • Manual literature curation is labor-intensive, requiring domain expertise and time to process vast numbers of scientific articles.

Purpose of the Study:

  • To develop and evaluate a supervised machine learning pipeline to automate and enhance the literature retrieval process for environmental exposure biomarkers.
  • To reduce the manual workload for database curators and improve the efficiency of identifying relevant scientific publications.

Main Methods:

  • A supervised machine learning approach was employed using a manually curated corpus from the Exposome-Explorer as training and testing data.
  • Various algorithms and parameters were assessed, utilizing article titles, abstracts, and metadata to predict relevance.
  • A separate classifier was trained for biomarker entity recognition to identify potential new database entries.

Main Results:

  • The optimal classifier, a Logistic Regression model using titles and abstracts, achieved an F2-score of 70.1% for relevance prediction.
  • The pipeline reduced the number of articles requiring manual screening by nearly 90%, with only a 22.1% misclassification rate of relevant articles.
  • 1,143 entities were extracted, leading to the manual validation of 45 new candidate entries for the database.

Conclusions:

  • The proposed machine learning methodology significantly enhances the efficiency of literature curation for environmental health databases.
  • This approach can be adapted for similar biomarker datasets, chemical databases, or disease-related information, streamlining data acquisition and discovery.