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Cited by 181Bioinformatics and Genomic NetworksComplex Network Analysis TechniquesGene Regulatory Network Analysis

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Geometric De-noising of Protein-Protein Interaction Networks

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Kuchaiev, Oleksii, Rašajski, Marija, Higham, Desmond J., Pržulj, Nataša·2009·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1000454PMC PMC2711306
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Abstract

Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.

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Figure 1 Probability density functions and . Probability density functions and learned from embedding the largest connected components of the following PPI networks into 5-dimensional Euclidean space: (A) the yeast S. cerevisiae high confidence PPI network [11] (“Yhigh”); (B) the human PPI network from BioGRID (version 2.0.35) [13] (“HumanBG”). The represents the values of the Euclidean distances between pairs of nodes in the embedding; the represents the values of probability density functions and .
Figure 1 Probability density functions and . Probability density functions and learned from embedding the largest connected components of the following PPI networks into 5-dimensional Euclidean space: (A) the yeast S. cerevisiae high confidence PPI network [11] (“Yhigh”); (B) the human PPI network from BioGRID (version 2.0.35) [13] (“HumanBG”). The represents the values of the Euclidean distances between pairs of nodes in the embedding; the represents the values of probability density functions and .CC BY · PubMed Central
Figure 2 ROC curves for “Yhigh” PPI network for embedding space dimensions of 2 to 7. ROC curves measuring the accuracy of de-noising procedure when applied to “Yhigh” PPI network using embedding space dimensions of 2 to 7. is 1–specificity and is sensitivity. Numbers in brackets correspond to the numbers of true positives and false positives for a given distance cutoff (TP,FP).
Figure 2 ROC curves for “Yhigh” PPI network for embedding space dimensions of 2 to 7. ROC curves measuring the accuracy of de-noising procedure when applied to “Yhigh” PPI network using embedding space dimensions of 2 to 7. is 1–specificity and is sensitivity. Numbers in brackets correspond to the numbers of true positives and false positives for a given distance cutoff (TP,FP).CC BY · PubMed Central
Figure 3 Precision versus Recall curves. Precision versus Recall curves for “Yhigh” PPI network for embedding space dimensions of 2 to 7. is recall and is precision.
Figure 3 Precision versus Recall curves. Precision versus Recall curves for “Yhigh” PPI network for embedding space dimensions of 2 to 7. is recall and is precision.CC BY · PubMed Central
Figure 4 ROC curves for “Yhigh” PPI network with and without iterating embedding and de-noising procedures. The first (blue) ROC curve shows the performance of the de-noising procedure applied to “Yhigh” PPI network using embedding space dimension of 4. The second (red) ROC curve shows the performance after iterating the embedding and de-noising procedures until convergence. is 1–specificity and is sensitivity.
Figure 4 ROC curves for “Yhigh” PPI network with and without iterating embedding and de-noising procedures. The first (blue) ROC curve shows the performance of the de-noising procedure applied to “Yhigh” PPI network using embedding space dimension of 4. The second (red) ROC curve shows the performance after iterating the embedding and de-noising procedures until convergence. is 1–specificity and is sensitivity.CC BY · PubMed Central
Figure 5 ROC curves for recovering deleted edges. ROC curves for the experiments in which 500, 1000, 2000 and 3000 edges from “Yhigh” network were removed at random and then recovered using the de-noising procedure. is 1–specificity and is sensitivity.
Figure 5 ROC curves for recovering deleted edges. ROC curves for the experiments in which 500, 1000, 2000 and 3000 edges from “Yhigh” network were removed at random and then recovered using the de-noising procedure. is 1–specificity and is sensitivity.CC BY · PubMed Central

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Metadata

Authors
Kuchaiev, O., Rašajski, M., Higham, D. J., & Pržulj, N.
Affiliations
  • University of California, Irvine (US)
  • University of Strathclyde (GB)
Corresponding author
Pržulj, Nataša
ORCID
Venue
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2009-08-07
Volume
5
Issue
8
Pages
e1000454
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1000454
PMC
PMC2711306
OpenAlex
W2050583899
Cited by
180
References
55
Citations by year
2026: 1 · 2025: 4 · 2024: 4 · 2023: 7 · 2022: 9 · 2021: 11 · 2020: 13 · 2019: 10 · 2018: 9 · 2017: 8 · 2016: 11 · 2015: 18 · 2014: 13 · 2013: 17 · 2012: 24
FWCI
6.03
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
Funders
  • National Science Foundation (0644424, CAREER)
  • Engineering and Physical Sciences Research Council (GR/S62383/01, EP/E049370/1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
protein-protein interaction network, geometric graphs, interaction prediction, false positive interactions, BioGRID, Gene Ontology terms, HPRD
MeSH terms
Algorithms, Humans, Models, Biological, Proteins, ROC Curve, Sensitivity and Specificity, Reproducibility of Results, Signal Transduction, Computational Biology, Protein Interaction Mapping, Databases, Protein

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