(1) Profile vectors are built by retrieving the Molecular Function Gene Ontology annotations (MF-GO terms) of Interpro domains from the file interpro2go. (2) From the profiles, a co-occurrence matrix is calculated by counting how many times two MF-GO terms occur in the same set of Interpro domains. (3) The co-occurrence vectors are feature vectors that describe the functional links of each MF-GO term. The similarity between the MF-GO terms is calculated by the cosine distance between the vectors. (4) The similarity values are arranged in a matrix . The similarity matrix was considered as the Adjacency Matrix of a weighted graph . The terms can be clustered by means of the partition of the graph. To obtain the best partition of , a Spectral Clustering algorithm is applied. The Spectral Clustering algorithm projects the terms in a K dimensional space which can be clustered with standard clustering techniques. (5) The GO terms are grouped in a Hierarchical Tree representing the Functional Distance that satisfy the mathematical properties of a Metric Space.<p><b>Copyright information:</b></p><p>Taken from "Defining functional distances over Gene Ontology"</p><p>http://www.biomedcentral.com/1471-2105/9/50</p><p>BMC Bioinformatics 2008;9():50-50.</p><p>Published online 25 Jan 2008</p><p>PMCID:PMC2375122.</p><p></p
