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Mining biological networks from full-text articles.

Jan Czarnecki1, Adrian J Shepherd

  • 1Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London, Malet Street, Bloomsbury, London, WC1E 7HX, UK.

Methods in Molecular Biology (Clifton, N.J.)
|May 3, 2014
PubMed
Summary
This summary is machine-generated.

Automated text mining can extract valuable protein-protein interaction (PPI) network data from scientific papers. This approach provides guidelines for building a pipeline to mine biological networks from full-text articles.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Biological systems are increasingly modeled as networks, including protein-protein interaction (PPI), metabolic, gene regulatory, and signaling networks.
  • Public databases contain some network data, but much relevant information remains embedded within full-text scientific literature.
  • There is a significant need for automated methods to extract this scattered network data.

Purpose of the Study:

  • To present practical guidelines for building a text-mining pipeline.
  • To enable automated extraction of protein-protein interaction (PPI) networks from full-text articles.
  • To provide a framework adaptable for mining other types of biological networks.

Main Methods:

  • Utilizing existing code and software components to construct a text-mining pipeline.
  • Focusing on the extraction of protein-protein interaction (PPI) networks as a primary application.
  • Developing a modular approach adaptable to different biological network types.

Main Results:

  • Demonstrated a practical methodology for constructing a text-mining pipeline.
  • Successfully outlined steps for extracting PPI networks from full-text scientific articles.
  • Established a flexible approach applicable to various biological network data mining tasks.

Conclusions:

  • Automated text mining offers an effective solution for consolidating scattered biological network data.
  • The presented guidelines facilitate the construction of pipelines for extracting PPI networks.
  • The methodology is adaptable for mining diverse biological networks, advancing systems biology research.