title: The role of indirect connections in gene networks in predicting function.
abstract: BACKGROUND
  Gene networks have been used widely in gene function prediction algorithms, many based on complex extensions of the 'guilt by association' principle. We sought to provide a unified explanation for the performance of gene function prediction algorithms in exploiting network structure and thereby simplify future analysis.
  
  
  RESULTS
  We use co-expression networks to show that most exploited network structure simply reconstructs the original correlation matrices from which the co-expression network was obtained. We show the same principle works in predicting gene function in protein interaction networks and that these methods perform comparably to much more sophisticated gene function prediction algorithms.
  
  
  METHODS
  Data and algorithm implementation are fully described and available at http://www.chibi.ubc.ca/extended. Programs are provided in Matlab m-code.
  
  
  BACKGROUND
  paul@chibi.ubc.ca
authors.affiliation: Centre for High-Throughput Biology and Department of Psychiatry, 177 Michael Smith Laboratories, 2185 East Mall, University of British Columbia, Vancouver, BC V6T1Z4, Canada.
authors.forename: Jesse
authors.initials: J
authors.lastname: Gillis
authors.forename: Paul
authors.initials: P
authors.lastname: Pavlidis
mesh_terms: D000465:Algorithms; D000818:Animals; D015870:Gene Expression; D053263:Gene Regulatory Networks; D006801:Humans; D051379:Mice; D011506:Proteins / Q000235:genetics; D015003:Yeasts / Q000235:genetics*
chemical_list: D011506:Proteins