Incorporating fuzzy semantic similarity measure in detecting human protein complexes in PPI network: A multiobjective approach

Detection of protein complexes within protein-protein interaction networks (PPIN) is a valuable step toward the analysis of biological processes and pathways. Several high-throughput experimental techniques produce large number of PPIs that can be extensively utilized for constructing PPI network of a species. Decomposition of the whole PPI network into smaller and manageable modules is an ongoing challenge. Here we have developed a multi-objective algorithm for detecting human protein complexes by partitioning large human PPI network into clusters which serve as protein complexes. Some graphical properties like density, centrality etc., are utilized for building the objectives. Besides the graphical properties we have also exploited a fuzzy measure based semantic similarity approach to construct similarity based objective. The proposed technique is demonstrated in the human PPI network and the resulting complexes are analyzed in context of Gene Ontology (GO) and pathway enrichment. We have also compared our results with that of some state-of-the-art algorithms in context of different performance metrics. The biological relevance of our predicted complexes are also established here by linking them with 22 key disease classes.