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Anne O'Tate: Value-added PubMed search engine for analysis and text mining.

Neil R Smalheiser1, Dean P Fragnito2, Eric E Tirk2

  • 1Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, United States of America.

Plos One
|March 8, 2021
PubMed
Summary

Anne O'Tate is an enhanced web tool for analyzing biomedical literature from PubMed. It now offers advanced features for sorting, ranking, and visualizing research results, aiding scientific discovery.

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Area of Science:

  • Biomedical Informatics
  • Bibliometrics
  • Computational Biology

Background:

  • PubMed is the primary search engine for biomedical literature.
  • User-driven summarization and mining of search results are crucial for researchers.
  • Existing tools may lack advanced functionalities for in-depth literature analysis.

Purpose of the Study:

  • To describe the expanded functionalities of the Anne O'Tate web tool.
  • To enhance user-driven analysis of PubMed search results.
  • To introduce new methods for identifying topic relationships and citation networks.

Main Methods:

  • The Anne O'Tate tool was updated with new sorting and ranking features.
  • Implemented machine learning for predicting publication types and study designs.
  • Integrated new tools: Mine the Gap! for under-represented topic pairs and Citation Cloud for citation network visualization.

Main Results:

  • Users can now sort and rank articles by phrases, author count, and co-occurring topics.
  • Articles are classified by NLM publication types and 50 predicted types/study designs.
  • Mine the Gap! identifies under-represented topic pairs, and Citation Cloud visualizes citation relationships.

Conclusions:

  • The expanded Anne O'Tate tool provides powerful new capabilities for biomedical literature analysis.
  • Novel features facilitate deeper insights into research trends and connections.
  • The tool supports diverse use cases in scientific research and discovery.