ArticleBMC bioinformatics2010
Bayesian statistical modelling of human protein interaction network incorporating protein disorder information.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Statistical genomics in rare cancer.Seminars in cancer biology · 2020Pooled it
- The bacteriostatic regulation of luteolin from honeysuckle by protein network interaction.Frontiers in bioinformatics · 2025Article
- Testing biological network motif significance with exponential random graph models.Applied network science · 2021Article
- Bayesian model of signal rewiring reveals mechanisms of gene dysregulation in acquired drug resistance in breast cancer.PloS one · 2017Article
- Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling.BMC systems biology · 2015Article
- Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles.Proteome science · 2013Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each node's contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely social, as revealed based on the local sociality parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: a priori knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network.
resultsWe applied the statistical modelling to the human protein interaction network obtained with Y2H experiments. Bayesian approach for the estimation of the parameters was employed. We deduced social proteins, essential for the formation of the network, while incorporating into the model information on protein disorder. Intrinsically disordered are proteins which lack a well-defined three-dimensional structure under physiological conditions. We predicted the fold group (ordered or disordered) of proteins in the network from their primary sequences. The network analysis indicated that protein disorder has a positive effect on the connectivity of proteins in the network, but do not fully explains the interactivity.
conclusionsThe approach opens a perspective to study effects of biological properties of individual entities on the structure of biological networks.
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