Parenclitic networks: a multilayer description of heterogeneous and static data-sets

Massimiliano Zanin, Joaquín Medina Alcazar, Jesus Vicente Carbajosa, David Papo, M Gomez Paez, Pedro Sousa, Ernestina Menasalvas, Stefano Boccaletti

Research output: Contribution to journalArticle

Abstract

Describing a complex system is in many ways a problem akin to identifying an object, in that it involves defining boundaries, constituent parts and their relationships by the use of grouping laws. Here we propose a novel method which extends the use of complex networks theory to a generalized class of non-Gestaltic systems, taking the form of collections of isolated, possibly heterogeneous, scalars, e.g. sets of biomedical tests. The ability of the method to unveil relevant information is illustrated for the case of gene expression in the response to osmotic stress of {\it Arabidopsis thaliana}. The most important genes turn out to be the nodes with highest centrality in appropriately reconstructed networks. The method allows predicting a set of 15 genes whose relationship with such stress was previously unknown in the literature. The validity of such predictions is demonstrated by means of a target experiment, in which the predicted genes are one by one artificially induced, and the growth of the corresponding phenotypes turns out to feature statistically significant differences when compared to that of the wild-type.

Parenclitic networks: a multilayer description of heterogeneous and static data-sets. Available from: https://www.researchgate.net/publication/236124491_Parenclitic_networks_a_multilayer_description_of_heterogeneous_and_static_data-sets [accessed Oct 13 2017].
Original languageEnglish
JournalarXiv preprint arXiv:1304.1896
Publication statusPublished - 2013

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