title: An ensemble framework for clustering protein-protein interaction networks.
abstract: BACKGROUND
  Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network.
  
  
  RESULTS
  In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science and Engineering, Ohio State University, USA.
authors.forename: Sitaram
authors.initials: S
authors.lastname: Asur
authors.forename: Duygu
authors.initials: D
authors.lastname: Ucar
authors.forename: Srinivasan
authors.initials: S
authors.lastname: Parthasarathy
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D008954:Models, Biological; D005810:Multigene Family / Q000502:physiology*; D025341:Principal Component Analysis; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome