Understanding diseases as increased heterogeneity: a complex network computational framework.

Owing to the complexity of the human body, most diseases present a high interpersonal variability in the way they manifest, i.e. in their phenotype, which has important clinical repercussions-for instance, the difficulty in defining objective diagnostic rules. Here we explore the hypothesis that signs and symptoms used to define a disease should be understood in terms of the dispersion (as opposed to the average) of physical observables. To that end, we propose a computational framework, based on complex networks theory, to map groups of subjects to a network structure, based on their pairwise phenotypical similarity. We demonstrate that the resulting structure can be used to improve the performance of classification algorithms, especially in the case of a limited number of instances, with both synthetic and real datasets. Beyond providing an alternative conceptual understanding of diseases, the proposed framework could be of special relevance in the growing field of personalized, or N-to-1, medicine.