Understanding diseases as increased heterogeneity: a complex network computational framework

TL;DR

This paper introduces a complex network-based computational framework to understand disease heterogeneity by analyzing phenotypical variability, improving classification, and supporting personalized medicine.

Contribution

It proposes a novel network approach to model disease heterogeneity based on phenotypical dispersion, enhancing classification especially with limited data.

Findings

Network structure improves classification accuracy

Method effective on synthetic and real datasets

Supports personalized medicine approaches

Abstract

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.