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Protein Networks02:26

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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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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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Handling Noise in Protein Interaction Networks.

Fernanda B Correia1,2, Edgar D Coelho1, José L Oliveira1

  • 1Department of Electronics, Telecommunications and Informatics, Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, 3810-193 Aveiro, Portugal.

Biomed Research International
|December 13, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new Organization Measurement (OM) method to denoise protein-protein interaction (PPI) networks and predict missing links. This topology-based approach effectively refines biological networks for yeast and human PPI data.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Network Science

Background:

  • Protein-protein interactions (PPIs) form complex networks crucial for cellular functions.
  • Studying PPI network topology can reveal organism-specific patterns.
  • Existing methods may struggle with noise and missing data in PPI networks.

Purpose of the Study:

  • To introduce a novel methodology, Organization Measurement (OM), for denoising PPI networks.
  • To predict missing links in PPI networks based solely on network topology.
  • To evaluate the OM method's efficacy on Saccharomyces cerevisiae and Homo sapiens PPI datasets.

Main Methods:

  • Application of the Organization Measurement (OM) methodology to PPI networks.
  • Denoising PPI networks from Saccharomyces cerevisiae (Yeast, CS2007) and Homo sapiens (Human) datasets.
  • Evaluation using random network comparisons, reference set analysis, and network perturbation (edge addition/removal).

Main Results:

  • High Area Under the Curve (AUC) values achieved: 0.95 (Yeast), 0.87 (Human), and 0.99 (CS2007).
  • Robust performance under network perturbations: AUCs of 0.71-0.75 for edge removal/addition in reference sets.
  • Effective identification and correction of false positives (up to 97%) and true positives (up to 95%) in the CS2007 dataset.

Conclusions:

  • The OM methodology demonstrates high sensitivity to the topological structure of biological networks.
  • The OM approach efficiently denoises PPI networks and predicts missing links.
  • This method offers a valuable tool for refining and analyzing biological network data.