  Semantic similarity measures (SSMs) refer to a set of algorithms used to
quantify the similarity of two or more terms belonging to the same ontology.
Ontology terms may be associated to concepts, for instance in computational
biology gene and proteins are associated with terms of biological ontologies.
Thus, SSMs may be used to quantify the similarity of genes and proteins
starting from the comparison of the associated annotations. SSMs have been
recently used to compare genes and proteins even on a system level scale. More
recently some works have focused on the building and analysis of Semantic
Similarity Networks (SSNs) i.e. weighted networks in which nodes represents
genes or proteins while weighted edges represent the semantic similarity score
among them. SSNs are quasi-complete networks, thus their analysis presents
different challenges that should be addressed. For instance, the need for the
introduction of reliable thresholds for the elimination of meaningless edges
arises. Nevertheless, the use of global thresholding methods may produce the
elimination of meaningful nodes, while the use of local thresholds may
introduce biases. For these aims, we introduce a novel technique, based on
spectral graph considerations and on a mixed global-local focus. The
effectiveness of our technique is demonstrated by using markov clustering for
the extraction of biological modules. We applied clustering to simplified
networks demonstrating a considerable improvements with respect to the original
ones.
