In this paper 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. Our work is motivated by an interest in constructing a protein-protein interaction network that captures key features associated with Parkinson's disease. The proposed method is based on a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by choosing a minimal seed list as  starting point for the significance test. The null model is based on random seed lists of  same length as a minimum seed list which 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 significantly deviate from random on an appropriate set of statistics and hence make suggestions as to which of the many network sampling methods might capture useful information for a real world protein-protein interaction network.
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.
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In this paper we
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
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Our work is motivated by an interest in constructing a protein-protein interaction network that captures key features associated with Parkinson's disease.
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is based on
uses
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choosing
constructing
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the
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the
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which
that
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approximately
(approximately)
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significantly deviate
deviate significantly
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hence make suggestions as to which of the many network sampling methods