Prediction of GO Terms Based on Partitioning PPI Networks into Highly Connected Components

This paper proposes a novel two-phase method for Gene Ontology (GO) term prediction, which integrates a Variable Neighborhood Search (VNS)-based strategy for partitioning biological networks with a dedicated procedure for functional annotation. In the first phase, we introduce a VNS metaheuristic for detecting highly connected components in protein-protein interaction (PPI) network, enabling the grouping of biologically correlated elements into cohesive subnetworks. In the second phase, we utilize the obtained network partitions for functional enrichment analysis, employing the DINGO annotation tool to predict GO terms. Experimental results on real-world and artificial datasets confirm the effectiveness of the proposed approach in producing high-quality network partitions and its potential to support accurate GO term annotation. Although demonstrated here on biological networks, the proposed VNS framework is general and applicable to any network where the identification of strongly connected modules is relevant.