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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
Summary
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.
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.
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