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PubRunner: A light-weight framework for updating text mining results.

Kishore R Anekalla1, J P Courneya2, Nicolas Fiorini3

  • 1Northwestern University, Chicago, IL, 60611, USA.

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|November 23, 2017
PubMed
Summary
This summary is machine-generated.

PubRunner is a framework that automates text mining on new biomedical publications, ensuring tools stay current. This supports biologists by providing up-to-date insights from the latest research, aiding hypothesis generation.

Keywords:
BioNLPPubMedPubRunnerbiomedical text miningnatural language processingtext mining

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Area of Science:

  • Biomedical Informatics
  • Computational Biology

Background:

  • Biomedical text mining aids researchers in understanding complex biological systems and generating hypotheses.
  • The rapid daily publication of new research necessitates up-to-date text mining corpora.
  • Current text mining tools often yield static results, limiting their utility as knowledge expands.

Purpose of the Study:

  • To present PubRunner, a framework for continuously updating biomedical text mining results.
  • To address the challenge of static results in underused text mining tools.
  • To facilitate the integration of text mining into routine biological research.

Main Methods:

  • Developed PubRunner, a lightweight and user-friendly framework for automated text mining.
  • Implemented a workflow involving downloading PubMed abstracts, executing user-defined tools, and publishing results.
  • Integrated with public repositories like FTP and Zenodo for data dissemination.
  • Created a public website to announce the availability of updated results.

Main Results:

  • Demonstrated PubRunner's proof of concept by re-running the word2vec tool on the latest PubMed abstracts.
  • Generated up-to-date word vector representations for the biomedical domain.
  • Successfully automated the process of updating text mining outputs.

Conclusions:

  • PubRunner provides a solution for keeping biomedical text mining tools current with the latest publications.
  • The framework is designed for easy integration and regular execution of text mining analyses.
  • Encourages the development of dynamic text mining tools to better support biological research and discovery.