  The last decade has seen the advent and consolidation of ontology based tools
for the identification and biological interpretation of classes of genes, such
as the Gene Ontology. The information accumulated time-by-time and included in
the GO is encoded in the definition of terms and in the setting up of semantic
relations amongst terms. This approach might be usefully complemented by a
bottom-up approach based on the knowledge of relationships amongst genes. To
this end, we investigate the Gene Ontology from a complex network perspective.
We consider the semantic network of terms naturally associated with the
semantic relationships provided by the Gene Ontology consortium and a
gene-based weighted network in which the nodes are the terms and a link between
any two terms is set up whenever genes are annotated in both terms. One aim of
the present paper is to understand whether the semantic and the gene-based
network share the same structural properties or not. We then consider network
communities. The identification of communities in the SVNs network can
therefore be the basis of a simple protocol aiming at fully exploiting the
possible relationships amongst terms, thus improving the knowledge of the
semantic structure of GO. This is also important from a biomedical point of
view, as it might reveal how genes over-expressed in a certain term also affect
other biological functions not directly linked by the GO semantics. As a
by-product, we present a simple methodology that allows to have a first glance
insight about the biological characterization of groups of GO terms.
