Network Distances for Weighted Digraphs

The interpretation of the biological mechanisms through the systems biology approach involves the representation of the molecular components in an integrated system, namely a network, where the interactions among them are much more informative than the single components. The definition of the dissimilarity between complex biological networks is fundamental to understand differences between conditions, states, and treatments. It is, therefore, challenging to identify the most suitable distance measures for this kind of analysis. In this work, we aim at testing several measures to define the distance among sample- and condition-specific metabolic networks. The networks are represented as directed, weighted graphs, due to the nature of the metabolic reactions. We used four different case studies and exploited Support Vector Machine classification to define the performance of each measure.