  In computational biology, biological entities such as genes or proteins are
usually annotated with terms extracted from Gene Ontology (GO). The functional
similarity among terms of an ontology is evaluated by using Semantic Similarity
Measures (SSM). More recently, the extensive application of SSMs yielded to the
Semantic Similarity Networks (SSNs). SSNs are edge-weighted graphs where the
nodes are concepts (e.g. proteins) and each edge has an associated weight that
represents the semantic similarity among related pairs of nodes. The analysis
of SSNs may reveal biologically meaningful knowledge. For these aims, the need
for the introduction of tool able to manage and analyze SSN arises.
Consequently we developed SSN-Analyzer a web based tool able to build and
preprocess SSN. As proof of concept we demonstrate that community detection
algorithms applied to filtered (thresholded) networks, have better performances
in terms of biological relevance of the results, with respect to the use of raw
unfiltered networks.
