title: Information assessment on predicting protein-protein interactions.
abstract: BACKGROUND
  Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.
  
  
  RESULTS
  Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.
  
  
  CONCLUSIONS
  In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.
authors.affiliation: Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA. nlin@math.wustl.edu <nlin@math.wustl.edu>
authors.forename: Nan
authors.initials: N
authors.lastname: Lin
authors.forename: Baolin
authors.initials: B
authors.lastname: Wu
authors.forename: Ronald
authors.initials: R
authors.lastname: Jansen
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D001185:Artificial Intelligence; D030562:Databases, Protein; D005656:Fungal Proteins / Q000378:metabolism*; D016681:Genome, Fungal; D023281:Genomics / Q000379:methods; D016015:Logistic Models; D008957:Models, Genetic; D011237:Predictive Value of Tests; D025941:Protein Interaction Mapping / Q000379:methods* / Q000706:statistics & numerical data; D020543:Proteome / Q000378:metabolism
chemical_list: D005656:Fungal Proteins; D020543:Proteome