Fig. 2

The clustering-based feature extraction approach consists of three stages. Namely, creation of affinity graph, clustering computation, and Gene Ontology term enrichment. Its inputs are a GCN, denoted by \(G=(V,E,w)\), a set of functions A, an annotation function \(\phi :V\rightarrow 2^A\), and a set \(K=\{k_0,\dots ,k_{m-1}\}\). Its output are two feature matrices (for both G and its enriched version F) of dimension \(V\times A\cdot K\rightarrow [0,1]\) that specify how likely it is for the genes to be associated to the functions in A when the graph is decomposed m clusters, each of size \(k_i\), for \(0\le i\le m\)