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An integrated text mining framework for metabolic interaction network reconstruction.

Preecha Patumcharoenpol1,2, Narumol Doungpan3, Asawin Meechai1,4

  • 1Systems Biology and Bioinformatics Laboratory, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.

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|March 29, 2016
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Summary

This study introduces a text mining framework to extract metabolic events and reconstruct biological networks. The open-source tool aids biologists in analyzing enzyme and metabolite interactions for metabolic pathway discovery.

Keywords:
CorpusIntegrated frameworkMetabolic entitiesMetabolic interaction networkText mining (TM)

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Systems Biology

Background:

  • Text mining (TM) is crucial for extracting biological entities and relationships.
  • Applying TM to metabolic interactions (enzyme-metabolite) through metabolic events is valuable.
  • Existing methods lack integrated frameworks for metabolic network reconstruction.

Purpose of the Study:

  • To present an integrated text mining framework for metabolic event extraction and network reconstruction.
  • To develop modules for metabolic event extraction (MEE) and metabolic interaction network reconstruction (MINR).
  • To provide an open-source tool for biologists to analyze metabolic interactions.

Main Methods:

  • Developed a two-module integrated TM framework: Metabolic Event Extraction (MEE) and Metabolic Interaction Network Reconstruction (MINR).
  • Evaluated MEE using the Metabolic Entities (ME) corpus for production and consumption events.
  • Tested entity taggers (Gene/Protein, metabolite) on a specific biosynthesis pathway.
  • Assessed MINR performance in mapping enzyme-metabolite interactions.
  • Applied the framework to large-scale EcoCyc data for network reconstruction.

Main Results:

  • MEE module achieved F-scores of 59.15% (production) and 48.59% (consumption).
  • Entity tagger achieved >80% F-score for Gene/Protein and metabolite identification in a specific pathway.
  • MINR module demonstrated >70% F-score for network reconstruction.
  • Large-scale application yielded precisions of 69.93% (enzyme), 70.63% (metabolite), and 46.71% (enzyme-metabolite interaction).

Conclusions:

  • The integrated TM framework effectively extracts metabolic events and reconstructs metabolic interaction networks.
  • The framework shows promising performance on both specific pathways and large-scale biological data.
  • This open-source tool facilitates the analysis of metabolic interactions and aids biological discovery.