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Comparing biological networks via graph compression.

Morihiro Hayashida1, Tatsuya Akutsu

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan. morihiro@kuicr.kyoto-u.ac.jp

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Novel algorithms CompressEdge and CompressVertices efficiently compare large biological networks by compressing their structures. These methods offer a reliable way to measure similarities between complex biological networks.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Network Biology

Background:

  • Comparing biological data, especially large networks, presents significant challenges in bioinformatics and systems biology.
  • Existing data compression methods face limitations in guaranteeing unique results for network comparison due to ambiguity in edge selection.

Purpose of the Study:

  • To develop novel and efficient methods for comparing large biological networks.
  • To address the difficulties in comparing global structures of biological networks.

Main Methods:

  • Proposed CompressEdge and CompressVertices algorithms compress network structures by iteratively contracting edges.
  • Network similarity is quantified using the compression ratio of concatenated networks.

Main Results:

  • Applied to metabolic networks of multiple organisms (e.g., H. sapiens, E. coli, S. cerevisiae).
  • Demonstrated efficient measurement of similarities between metabolic networks.
  • Outperformed existing methods in comparative analysis.

Conclusions:

  • The proposed node-labeled network compression algorithms are effective for measuring the similarity of large biological networks.
  • These methods provide a robust approach for biological network comparison.