ArticleBMC bioinformatics2012
HIDEN: Hierarchical decomposition of regulatory networks.
Article in BMC bioinformatics, 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.
- Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast.BMC systems biology · 2017Article
- Hierarchical decomposition of dynamically evolving regulatory networks.BMC bioinformatics · 2015Article
- Signal reachability facilitates characterization of probabilistic signaling networks.BMC bioinformatics · 2015Article
- Large scale analysis of signal reachability.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTranscription factors regulate numerous cellular processes by controlling the rate of production of each gene. The regulatory relations are modeled using transcriptional regulatory networks. Recent studies have shown that such networks have an underlying hierarchical organization. We consider the problem of discovering the underlying hierarchy in transcriptional regulatory networks.
resultsWe first transform this problem to a mixed integer programming problem. We then use existing tools to solve the resulting problem. For larger networks this strategy does not work due to rapid increase in running time and space usage. We use divide and conquer strategy for such networks. We use our method to analyze the transcriptional regulatory networks of E. coli, H. sapiens and S. cerevisiae.
conclusionsOur experiments demonstrate that: (i) Our method gives statistically better results than three existing state of the art methods; (ii) Our method is robust against errors in the data and (iii) Our method's performance is not affected by the different topologies in the data.
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Registered trials
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