A Multi-Objective Evolutionary Algorithm Integrating Topological and Gene Ontology Information for Overlapping...

Accurate identification of protein complexes is essential for understanding the structural and functional organization of cells. Since many proteins participate in multiple complexes, protein complex detection should account for overlapping modular structures rather than assuming disjoint communities. However, many existing computational methods still rely mainly on the topological properties of protein-protein interaction (PPI) networks, which may limit their biological interpretability and robustness in noisy networks. To address this challenge, we propose MOEGO-OPCD, a multi-objective evolutionary framework for overlapping protein complex detection that jointly exploits network topology and Gene Ontology (GO)-based functional information. The proposed framework first represents candidate solutions using an edge-based encoding scheme, then decodes them into overlapping node complexes, and subsequently optimizes them within a decomposition-based multi-objective evolutionary process. Three complementary objectives are considered to promote intra-complex topological compactness, penalize inter-complex topological dispersion, and enhance GO-based functional coherence. To improve the search process, the framework incorporates an objective-guided uniform crossover operator and a self-adaptive heuristic mutation operator that uses both structural and functional cues to balance exploration and exploitation. Extensive experiments on three benchmark yeast PPI networks, Yeast-D1, Yeast-D2, and Collins, evaluated against the MIPS and CYC2008 reference complexes, show that the proposed method achieves consistently strong and stable performance and outperforms a range of existing baselines in terms of Recall, Precision, and F-score. Additional sensitivity and runtime analyses further demonstrate that the framework is robust across a broad range of parameter settings and scales reasonably across the tested datasets. These results indicate that biologically informed multi-objective evolutionary optimization provides an effective and interpretable strategy for detecting overlapping protein complexes in PPI networks.