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Describing a complex system is in many ways a problem akin to identifying anobject, in that it involves defining boundaries, constituent parts and theirrelationships by the use of grouping laws. Here we propose a novel method whichextends the use of complex networks theory to a generalized class ofnon-Gestaltic systems, taking the form of collections of isolated, possiblyheterogeneous, scalars, e.g. sets of biomedical tests. The ability of themethod to unveil relevant information is illustrated for the case of geneexpression in the response to osmotic stress of {\it Arabidopsis thaliana}. Themost important genes turn out to be the nodes with highest centrality inappropriately reconstructed networks. The method allows predicting a set of 15genes whose relationship with such stress was previously unknown in theliterature. The validity of such predictions is demonstrated by means of atarget experiment, in which the predicted genes are one by one artificiallyinduced, and the growth of the corresponding phenotypes turns out to featurestatistically significant differences when compared to that of the wild-type.

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