Abstract
Detecting protein complexes is crucial in computational biology for understanding cellular mechanisms and facilitating drug discovery. Evolutionary algorithms (EAs) have proven effective in uncovering protein complexes within networks of protein-protein interactions (PPIs). However, their integration with functional insights from gene ontology (GO) annotations remains underexplored. This paper presents two primary contributions: First, it proposes a novel multi-objective optimization model for detecting protein complexes, conceptualizing the task as a problem with inherently conflicting objectives based on biological data. Second, it introduces an innovative gene ontology-based mutation operator, termed the Functional Similarity-Based Protein Translocation Operator ([Formula: see text]). This operator enhances collaboration between the canonical model and the GO-informed mutation strategy, thereby improving the algorithm's performance. As far as we know, this is the initial effort to incorporate the biological characteristics of PPIs into both the problem formulation and the development of intricate perturbation strategies. We assess the effectiveness of the proposed multi-objective evolutionary algorithm through experiments conducted on two widely recognized PPI networks and two standard complex datasets provided by the Munich Information Center for Protein Sequences (MIPS). To further assess the robustness of our algorithm, we create artificial networks by introducing different noise levels into the original Saccharomyces cerevisiae (yeast) PPI networks. This allows us to evaluate how perturbations in protein interactions affect the algorithm's performance compared to other approaches. The experimental results highlight that our algorithm outperforms several state-of-the-art methods in accurately identifying protein complexes. Moreover, the findings emphasize the substantial advantages of incorporating our heuristic perturbation operator, which significantly improves the quality of the detected complexes over other evolutionary algorithm-based methods.