  Due to the complexity of the human body, most diseases present a high
inter-personal variability in the way they manifest, i.e. in their phenotype,
which has important clinical repercussions - as for instance the difficulty in
defining objective diagnostic rules. We here 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, both with synthetic and real data sets. Beyond
providing an alternative conceptual understanding of diseases, the proposed
framework could be of special relevance in the growing field of personalised,
or N-to-1, medicine.
