ArticlePloS one2012
Network compression as a quality measure for protein interaction networks.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 15 citations in OpenAlex.
- OpenCell: Endogenous tagging for the cartography of human cellular organization.Science (New York, N.Y.) · 2022Article
- msVolcano: A flexible web application for visualizing quantitative proteomics data.Proteomics · 2016Article
- Affinity proteomics to study endogenous protein complexes: pointers, pitfalls, preferences and perspectives.BioTechniques · 2015Article
- Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis.Nucleic acids research · 2014Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients.
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