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Fair evaluation of global network aligners.

Joseph Crawford1, Yihan Sun2, Tijana Milenković1

  • 1Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications (iCeNSA), ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556 USA.

Algorithms for Molecular Biology : AMB
|June 11, 2015
PubMed
Summary

Biological network alignment combines node cost functions (NCF) and alignment strategies (AS). We found MI-GRAAL's NCF outperforms GHOST's, while AS performance varies by data, suggesting combinations can yield superior methods for conserved network region identification.

Keywords:
Across-species protein function predictionNetwork alignmentNetwork similarityProtein–protein interaction networks

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Biological network alignment identifies conserved regions across species, enabling function transfer.
  • Existing methods combine node cost functions (NCF) and alignment strategies (AS), but optimal combinations are unclear.
  • This study evaluates MI-GRAAL and GHOST, assessing NCF-AS combinations and parameter choices.

Purpose of the Study:

  • To determine the optimal combination of NCF and AS for biological network alignment.
  • To investigate the impact of sequence vs. topology data in NCF on alignment quality.
  • To assess the effect of neighborhood size in NCF on alignment performance.

Main Methods:

  • Comparative analysis of MI-GRAAL and GHOST network alignment algorithms.
  • Systematic evaluation of NCF-AS combinations using biological network data.
  • Parameter sensitivity analysis for sequence/topology ratio and neighborhood size within NCF.

Main Results:

  • MI-GRAAL's NCF is superior to GHOST's; AS performance is data-dependent.
  • Combinations of MI-GRAAL's NCF with GHOST's AS can yield superior alignment methods.
  • Topological information in NCF is crucial, while the sequence/topology ratio has minimal impact.
  • Larger neighborhood sizes are generally preferred, with the second largest often optimal.

Conclusions:

  • The study provides general recommendations for evaluating network alignment methods.
  • Highlights the benefit of combining NCF and AS from different methods for improved performance.
  • Suggests optimal parameter choices for NCF, balancing alignment quality and computational complexity.