ArticleEURASIP journal on bioinformatics & systems biology2012
Relations between the set-complexity and the structure of graphs and their sub-graphs.
Article in EURASIP journal on bioinformatics & systems biology, 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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Who cites it
4 citing papers in PubMed.
- Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology.Journal of clinical medicine · 2019Article
- The Information Content of Discrete Functions and Their Application in Genetic Data Analysis.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Biological data analysis as an information theory problem: multivariable dependence measures and the shadows algorithm.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Describing the complexity of systems: multivariable "set complexity" and the information basis of systems biology.Journal of computational biology : a journal of computational molecular cell biology · 2014Article
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Authors and funding
3 authors.
Funding
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Abstract
: We describe some new conceptual tools for the rigorous, mathematical description of the "set-complexity" of graphs. This set-complexity has been shown previously to be a useful measure for analyzing some biological networks, and in discussing biological information in a quantitative fashion. The advances described here allow us to define some significant relationships between the set-complexity measure and the structure of graphs, and of their component sub-graphs. We show here that modular graph structures tend to maximize the set-complexity of graphs. We point out the relationship between modularity and redundancy, and discuss the significance of set-complexity in this regard. We specifically discuss the relationship between complexity and entropy in the case of complete-bipartite graphs, and present a new method for constructing highly complex, binary graphs. These results can be extended to the case of ternary graphs, and to other multi-edge graphs, which are fundamentally more relevant to biological structures and systems. Finally, our results lead us to an approach for extracting high complexity modular graphs from large, noisy graphs with low information content. We illustrate this approach with two examples.
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Registered trials
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