ArticleBMC genomics2005
Effect of dataset selection on the topological interpretation of protein interaction networks.
Article in BMC genomics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Phosphotyrosine signaling proteins that drive oncogenesis tend to be highly interconnected.Molecular & cellular proteomics : MCP · 2013Article
- Chromatin regulation and gene centrality are essential for controlling fitness pleiotropy in yeast.PloS one · 2009Article
- Questioning the ubiquity of neofunctionalization.PLoS computational biology · 2009Article
- Preferential protection of protein interaction network hubs in yeast: evolved functionality of genetic redundancy.Proceedings of the National Academy of Sciences of the United States of America · 2008Article
- Protein interactions from complexes: a structural perspective.Comparative and functional genomics · 2007Article
- The effects of incomplete protein interaction data on structural and evolutionary inferences.BMC biology · 2006Article
- Yeast Protein Interactome topology provides framework for coordinated-functionality.Nucleic acids research · 2006Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundStudies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately.
resultsWe show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases.
conclusionWhen performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected.
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Registered trials
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