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Related Concept Videos

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Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks.

Le Yang1, Runpu Chen1, Thomas Melendy1

  • 1Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY 14203, USA.

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This study introduces MultiFDRnet, a novel method for identifying cancer pathways using multiplex networks. It effectively detects perturbed subnetworks by integrating multiple protein-protein interaction networks for improved accuracy.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Cancer Genomics

Background:

  • Large-scale cancer genome studies aim to identify cancer driver genes and molecular pathways.
  • Network-based methods use genomics data and protein-protein interaction (PPI) network topology to detect perturbed subnetworks as potential cancer pathways.
  • Variability in PPI network structures and incompleteness in context-specific networks challenge existing subnetwork detection algorithms.

Purpose of the Study:

  • To propose a novel method, MultiFDRnet, to address the limitations of existing subnetwork detection algorithms in cancer pathway identification.
  • To develop a method that can effectively utilize multiple PPI networks and handle incomplete topological structures.
  • To improve the accuracy and reliability of detecting cancer-related subnetworks.

Main Methods:

  • Modeled a set of PPI networks as a multiplex network to preserve individual network topology and introduce inter-network dependencies.
  • Developed MultiFDRnet to detect significantly perturbed subnetworks by simultaneously using all structural information from the multiplex network.
  • Incorporated genomics data with the integrated topological information of multiple PPI networks.

Main Results:

  • Benchmark analysis on simulated and real cancer data demonstrated the effectiveness of MultiFDRnet.
  • The method successfully detected significantly perturbed subnetworks supported by multiple PPI networks.
  • MultiFDRnet identified novel modular structures within context-specific PPI networks.

Conclusions:

  • MultiFDRnet offers a robust approach for identifying cancer pathways by integrating information from multiple PPI networks.
  • The multiplex network modeling effectively addresses challenges posed by varying and incomplete network structures.
  • The method enhances the discovery of cancer-related molecular subnetworks and potential therapeutic targets.