Motivation : If biology is modular then clusters, or communities, of proteins derived using only protein-protein interaction network structure might define protein modules with similar biological roles. We investigate the connection between biological modules and network communities in yeast and ask how the functional similarity of the communities that we find depends on the scales at which we probe the network. Results: We find many proteins lie in functionally homogeneous communities (a maximum of 2777 out of 4028 proteins) which suggests that network structure does indeed help identify sets of proteins with similar functions. The homogeneity of the communities depends on the scale selected . We use a novel test and two independent characterizations of protein function to determine the functional homogeneity of communities. We exploit the connection between network structure and biological function to select groups of proteins which are likely to participate in similar biological functions . We show that  high mean clustering coefficient and low mean node betweenness centrality can be used to predict functionally homogeneouscommunities. Availability: All the data sets and the community detection algorithm are available online .
Background : If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results: Our results demonstrate that the functional homogeneity of communities depends on the scale selected , and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function , and find a high degree of overlap between these measures . We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions: We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous .
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to determine the functional homogeneity of communities. We exploit the connection between network structure and biological function to select groups of proteins which are likely to participate in similar biological functions
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and low mean node betweenness centrality
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predict functionally homogeneouscommunities. Availability: All the data sets and the community detection algorithm are available online
identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions: We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous