title: Graphlet-based edge clustering reveals pathogen-interacting proteins.
abstract: BACKGROUND
  Prediction of protein function from protein interaction networks has received attention in the post-genomic era. A popular strategy has been to cluster the network into functionally coherent groups of proteins and assign the entire cluster with a function based on functions of its annotated members. Traditionally, network research has focused on clustering of nodes. However, clustering of edges may be preferred: nodes belong to multiple functional groups, but clustering of nodes typically cannot capture the group overlap, while clustering of edges can. Clustering of adjacent edges that share many neighbors was proposed recently, outperforming different node clustering methods. However, since some biological processes can have characteristic 'signatures' throughout the network, not just locally, it may be of interest to consider edges that are not necessarily adjacent.
  
  
  RESULTS
  We design a sensitive measure of the 'topological similarity' of edges that can deal with edges that are not necessarily adjacent. We cluster edges that are similar according to our measure in different baker's yeast protein interaction networks, outperforming existing node and edge clustering approaches. We apply our approach to the human network to predict new pathogen-interacting proteins. This is important, since these proteins represent drug target candidates.
  
  
  BACKGROUND
  Software executables are freely available upon request.
  
  
  BACKGROUND
  tmilenko@nd.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
authors.forename: R W
authors.initials: RW
authors.lastname: Solava
authors.forename: R P
authors.initials: RP
authors.lastname: Michaels
authors.forename: T
authors.initials: T
authors.lastname: Milenkovic
mesh_terms: D016000:Cluster Analysis; D054884:Host-Pathogen Interactions; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
chemical_list: D029701:Saccharomyces cerevisiae Proteins