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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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: Rašajski, Marija
Higham, Desmond J.
Pržulj, Nataša
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
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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Geometric De-noising of Protein-Protein Interaction Networks

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