title: Survey: Enhancing protein complex prediction in PPI networks with GO similarity weighting.
abstract: Predicting protein complexes from protein-protein interaction (PPI) networks has been the focus of many computational approaches over the last decade. These methods tend to vary in performance based on the structure of the network and the parameters provided to the algorithm. Here, we evaluate the merits of enhancing PPI networks with semantic similarity edge weights using Gene Ontology (GO) and its annotation data. We compare the cluster features and predictive efficacy of six well-known unweighted protein complex detection methods (Clique Percolation, MCODE, DPClus, IPCA, Graph Entropy, and CoAch) against updated weighted implementations. We conclude that incorporating semantic similarity edge weighting in PPI network analysis unequivocally increases the performance of these methods.
authors.affiliation: Department of Computer Science, Baylor University, Waco, TX, 76798, USA.
authors.forename: True
authors.initials: T
authors.lastname: Price
authors.forename: Francisco I
authors.initials: FI
authors.lastname: Peña
authors.forename: Young-Rae
authors.initials: YR
authors.lastname: Cho
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps