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Comparative network analysis via differential graphlet communities.

Serene W H Wong1, Nick Cercone, Igor Jurisica

  • 1Department of Computer Science and Engineering, York University, Toronto, Canada; Princess Margaret Cancer Centre, TECHNA Institute for the Advancement of Technology for Health, UHN, Toronto, Canada.

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|October 7, 2014
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Summary
This summary is machine-generated.

This study introduces a novel method to compare biological networks, identifying deregulated subgraphs in non-small cell lung cancer. Tumor cells create "shortcuts" in biological processes, offering insights into disease mechanisms and potential treatments.

Keywords:
Comparative network analysisDifferential graphlet communitiesNon-small cell lung cancerSystems Biology

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Current protein interaction data lacks condition-specific details, limiting understanding of disease mechanisms.
  • Existing methods for comparing healthy and disease networks are incomplete or lack systematic analysis of differential network structures.
  • mRNA abundance data offers potential for modeling condition-specific transcriptional changes and disease mechanisms.

Purpose of the Study:

  • To propose a novel method for efficiently comparing graphs by exploiting network structure information.
  • To introduce the concept of differential graphlet communities for detecting deregulated subgraphs.
  • To validate the method using non-small cell lung cancer datasets and analyze network structure differences.

Main Methods:

  • Developed a novel approach using differential graphlet communities to compare graphs.
  • Exploited network structure information by analyzing shortest path distributions on identified deregulated subgraphs.
  • Validated the method on multiple non-small cell lung cancer datasets.

Main Results:

  • The differential graphlet community approach systematically captures network structure differences between graphs.
  • Shortest path lengths were significantly longer in normal graphs compared to tumor graphs within differential graphlet communities.
  • Identified that tumor cells may create "shortcuts" between biological processes not present in normal conditions.

Conclusions:

  • The proposed method effectively exploits network structure information for comparative graph analysis.
  • Findings suggest significant alterations in biological process connectivity in non-small cell lung cancer.
  • The identification of "shortcuts" provides new avenues for understanding and potentially treating cancer.