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Optimising orbit counting of arbitrary order by equation selection.

Ine Melckenbeeck1, Pieter Audenaert2,3, Thomas Van Parys4,5,6

  • 1Ghent University - imec, IDLab, Technologiepark 15, Ghent, 9052, Belgium.

BMC Bioinformatics
|January 17, 2019
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Summary
This summary is machine-generated.

The Jesse algorithm optimizes graphlet counting for bioinformatics networks by selecting efficient equations based on graph density, improving performance up to twofold. This enhancement makes network analysis faster and more accessible.

Keywords:
Cytoscape appEquationsGraph theoryGraphletsOptimisationOrbits

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Area of Science:

  • Bioinformatics
  • Network Analysis
  • Computational Biology

Background:

  • Graphlets are crucial for analyzing biological networks.
  • The Jesse algorithm, inspired by ORCA, counts graphlets of any order by automatically generating equations.
  • Jesse's efficiency depends on equation selection, necessitating an investigation into optimal strategies.

Purpose of the Study:

  • To identify the most efficient equations for the Jesse graphlet counting algorithm.
  • To determine factors influencing the efficiency of equation selection.
  • To optimize Jesse's performance for bioinformatics network analysis.

Main Methods:

  • Developed the Jesse algorithm for graphlet orbit counting.
  • Implemented automatic generation of internal structures and equations.
  • Investigated the impact of different equation types and graph densities on runtime.

Main Results:

  • Jesse's runtime improved up to twofold with optimized equation selection compared to random choices.
  • Equation efficiency is primarily dependent on graph density, not graph type.
  • At low densities, equations with fewer terms are efficient; at high densities, equations with more terms are efficient.

Conclusions:

  • The Jesse algorithm achieves up to a twofold increase in efficiency through density-based automatic equation selection.
  • The optimized Jesse algorithm is available as a Cytoscape App for bioinformaticians.
  • This advancement facilitates more efficient graphlet analysis in bioinformatics.