title: Predicting protein phenotypes based on protein-protein interaction network.
abstract: BACKGROUND
  Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins.
  
  
  RESULTS
  Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%.
  
  
  CONCLUSIONS
  The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms.
authors.affiliation: Institute of Systems Biology, Shanghai University, Shanghai, China.
authors.forename: Lele
authors.initials: L
authors.lastname: Hu
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.forename: Xiao-Jun
authors.initials: XJ
authors.lastname: Liu
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
mesh_terms: D030562:Databases, Protein; D010641:Phenotype; D011485:Protein Binding; D025941:Protein Interaction Mapping; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*
chemical_list: D029701:Saccharomyces cerevisiae Proteins