Semantic similarity measures (SSMs) are used to evaluate the similarity among terms of an ontology. Biological entities, e.g., gene products, are often annotated with terms extracted from existing ontologies. A common application is to find the similarity or dissimilarity among two entities through the application of SSMs to their annotations. More recently, researchers have introduced the semantic similarity networks (SSNs), i.e., 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. Community detection algorithms that analyze SSNs may reveal clusters of functionally associated concepts. For instance, the application of these algorithms on networks built upon of proteins may find protein complexes. SSNs have a high number of arcs with low weight. The application of classical community detection algorithms on raw networks exhibits low performance. To improve the performance of such algorithms, a possible approach is to simplify the structure of SSNs through a preprocessing step able to delete arcs likened to noise. Thus, we propose a novel preprocessing strategy to simplify SSNs implemented in an open-source tool: SSN-Analyzer. 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.
Using SSN-Analyzer for analysis of semantic similarity networks PH Guzzi, M Milano, P Veltri, M Cannataro Network Modeling Analysis in Health Informatics and Bioinformatics 4 (1), 1-10
|Titolo:||Using SSN-Analyzer for analysis of semantic similarity networks|
|Data di pubblicazione:||2015|
|Appare nelle tipologie:||1.1 Articolo in rivista|