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Creating, generating and comparing random network models with NetworkRandomizer.

Gabriele Tosadori1,2, Ivan Bestvina3, Fausto Spoto1

  • 1Department of Computer Science, University of Verona, Verona, 37134, Italy.

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|December 5, 2017
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Summary
This summary is machine-generated.

This study introduces a Cytoscape app for generating randomized biological networks, aiding in the validation of complex biological datasets and providing a standardized approach for network analysis.

Keywords:
cytoscapenetwork analysisnetwork generationnetwork randomisationrandom networks

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Biological networks are crucial for analyzing high-throughput data in biology and biotechnology.
  • Network-based models are increasingly important, necessitating advanced data mining and validation techniques.

Purpose of the Study:

  • To address the validation gap in biological network analysis.
  • To provide researchers with tools for creating randomized networks and validating real biological datasets within the Cytoscape platform.

Main Methods:

  • Development of a Cytoscape app for generating randomized networks.
  • Implementation of established random network models as benchmarks.
  • Proposal of a novel multiplication algorithm for creating random weighted networks from quantitative data.

Main Results:

  • The app enables the creation of randomized networks for benchmarking.
  • A new method for generating random weighted networks is introduced.
  • Statistical tools are provided to compare real and random network attributes for validation.

Conclusions:

  • The developed Cytoscape app offers a standardized methodology for validating biological network analysis results.
  • The tool facilitates robust validation of biological datasets using random network models.
  • It enhances the reliability of findings derived from network-based biological research.