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Identifying communities from multiplex biological networks by randomized optimization of modularity.

Gilles Didier1, Alberto Valdeolivas1,2,3, Anaïs Baudot1,3

  • 1Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France.

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Summary

This study enhances MolTi software for biological network analysis. New features improve the detection of disease-related communities, aiding in understanding genetic associations with diseases.

Keywords:
Biological NetworksClusteringCommunity identificationDREAM challengeMulti-layerMultiplex

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Area of Science:

  • Computational Biology
  • Network Analysis
  • Bioinformatics

Background:

  • Community detection is crucial for analyzing large biological networks.
  • The Disease Module Identification (DMI) DREAM challenge benchmarked clustering methods for biomedical relevance.
  • Evaluating community associations with GWAS-derived genes is key for disease gene discovery.

Purpose of the Study:

  • To implement extensions to the MolTi software for improved community detection in biological networks.
  • To evaluate the performance of these enhanced MolTi features on simulated and benchmark datasets.
  • To assess the impact of randomization, edge weights, and layer weights on detecting trait and disease communities.

Main Methods:

  • Extended MolTi software with a randomized Louvain algorithm, edge/layer weighting, and recursive clustering.
  • Tested performance on simulated networks to validate community detection improvements.
  • Applied enhanced MolTi to the DMI DREAM challenge benchmark datasets.

Main Results:

  • Randomized Louvain algorithm significantly improved community detection on simulated networks.
  • Performance on the DMI benchmark varied with Genome-Wide Association Studies (GWAS) datasets and enrichment thresholds.
  • Weighted edges/layers and randomization generally increased the number of detected trait and disease communities.

Conclusions:

  • The enhanced MolTi software offers improved capabilities for biological network community detection.
  • The choice of GWAS dataset and statistical threshold critically influences results in disease module identification.
  • The implemented features provide a more robust approach to identifying disease-associated modules in biological networks.