title: Predicting protein function from protein/protein interaction data: a probabilistic approach.
abstract: BACKGROUND
  The development of experimental methods for genome scale analysis of molecular interaction networks has made possible new approaches to inferring protein function. This paper describes a method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network. The method exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors. A binomial model of local neighbor function labeling probability is combined with a Markov random field propagation algorithm to assign function probabilities for proteins in the network.
  
  
  RESULTS
  We applied the method to a protein-protein interaction dataset for the yeast Saccharomyces cerevisiae using the Gene Ontology (GO) terms as function labels. The method reconstructed known GO term assignments with high precision, and produced putative GO assignments to 320 proteins that currently lack GO annotation, which represents about 10% of the unlabeled proteins in S. cerevisiae.
authors.affiliation: Bioinformatics Program and Department of Biomedical Engineering, Boston University, 44 Cummington St., Boston, MA 02215, USA. sletovsky@aol.com
authors.forename: Stanley
authors.initials: S
authors.lastname: Letovsky
authors.forename: Simon
authors.initials: S
authors.lastname: Kasif
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D008954:Models, Biological; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000145:classification* / Q000378:metabolism*; D013329:Structure-Activity Relationship
chemical_list: D029701:Saccharomyces cerevisiae Proteins