ArticleJournal of the Royal Society, Interface2012
Graph spectral analysis of protein interaction network evolution.
Article in Journal of the Royal Society, Interface, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data.PLoS computational biology · 2026Article
- Rare event ABC-SMCStatistics and computing · 2026Article
- Gaining confidence in inferred networks.Scientific reports · 2022Article
- Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds.Journal of the Royal Society, Interface · 2020Article
- Flexible model selection for mechanistic network models.Journal of complex networks · 2020Article
- A nonparametric significance test for sampled networks.Bioinformatics (Oxford, England) · 2018Article
- Graphettes: Constant-time determination of graphlet and orbit identity including (possibly disconnected) graphlets up to size 8.PloS one · 2017Article
- Proper evaluation of alignment-free network comparison methods.Bioinformatics (Oxford, England) · 2015Article
- Revealing the hidden language of complex networks.Scientific reports · 2014Article
- A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.Nature protocols · 2014Article
- Approximate Bayesian inference for complex ecosystems.F1000prime reports · 2014Review
- Evolutionary Model Selection and Parameter Estimation for Protein-Protein Interaction Network Based on Differential Evolution Algorithm.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We present an analysis of protein interaction network data via the comparison of models of network evolution to the observed data. We take a bayesian approach and perform posterior density estimation using an approximate bayesian computation with sequential Monte Carlo method. Our approach allows us to perform model selection over a selection of potential network growth models. The methodology we apply uses a distance defined in terms of graph spectra which captures the network data more naturally than previously used summary statistics such as the degree distribution. Furthermore, we include the effects of sampling into the analysis, to properly correct for the incompleteness of existing datasets, and have analysed the performance of our method under various degrees of sampling. We consider a number of models focusing not only on the biologically relevant class of duplication models, but also including models of scale-free network growth that have previously been claimed to describe such data. We find a preference for a duplication-divergence with linear preferential attachment model in the majority of the interaction datasets considered. We also illustrate how our method can be used to perform multi-model inference of network parameters to estimate properties of the full network from sampled data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.