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Assisting document triage for human kinome curation via machine learning.

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This summary is machine-generated.

This study introduces an automated document triage system to efficiently curate bio-literature, specifically human kinase proteins. The system significantly improves the retrieval of articles with disease and biological process information, aiding biocurators.

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Area of Science:

  • Biomedical Informatics
  • Bioinformatics
  • Data Science

Background:

  • The rapid increase in scientific publications poses challenges for bio-literature database curation.
  • Efficiently retrieving relevant articles for tasks like human kinome curation is crucial.

Purpose of the Study:

  • To design and evaluate an automated document triage system for bio-literature curation.
  • To improve the efficiency and accuracy of retrieving articles related to human kinase proteins and their associated diseases and biological processes.

Main Methods:

  • Developed a document triage system incorporating text mining and machine learning.
  • Utilized co-occurrence and linguistic features to model the curation process.
  • Evaluated system performance on the BioCreative VI human kinome curation task.

Main Results:

  • The system effectively retrieved relevant kinase articles with disease (DIS) and biological process (BP) information.
  • Achieved higher Mean Average Precision (MAP) for DIS (0.458) and BP (0.195) compared to the neXtA5 system.
  • Demonstrated significant improvements in prioritizing kinome-related articles.

Conclusions:

  • The developed automated system enhances the efficiency of bio-literature curation workflows.
  • The system shows potential to assist biocurators in databases like neXtProt by improving article prioritization.
  • Text mining and machine learning approaches are effective for complex bio-curation tasks.