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Geometric De-noising of Protein-Protein Interaction Networks
Open accessAbstract
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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“Yeast two hybrid (Y2H), affinity-purification mass spectrometry (APMS), and other high-throughput experimental approaches have identified many pairs of interacting proteins in yeast and other organisms (1–5), but there are discrepancies between sets generated using the different methods and considerable false positive and false negative rates (6–8).”
From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [6]
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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
- 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
- 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
- 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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