  Our work is motivated by an interest in constructing a protein-protein
interaction network that captures key features associated with Parkinson's
disease. While there is an abundance of subnetwork construction methods
available, it is often far from obvious which subnetwork is the most suitable
starting point for further investigation. We provide a method to assess whether
a subnetwork constructed from a seed list (a list of nodes known to be
important in the area of interest) differs significantly from a randomly
generated subnetwork. The proposed method uses a Monte Carlo approach. As
different seed lists can give rise to the same subnetwork, we control for
redundancy by constructing a minimal seed list as the starting point for the
significance test. The null model is based on random seed lists of the same
length as a minimum seed list that generates the subnetwork, in this random
seed list the nodes have (approximately) the same degree distribution as the
nodes in the minimum seed list. We use this null model to select subnetworks
which deviate significantly from random on an appropriate set of statistics and
might capture useful information for a real world protein-protein interaction
network.
