title: Physical protein-protein interactions predicted from microarrays.
abstract: BACKGROUND
  Microarray expression data reveal functionally associated proteins. However, most proteins that are associated are not actually in direct physical contact. Predicting physical interactions directly from microarrays is both a challenging and important task that we addressed by developing a novel machine learning method optimized for this task.
  
  
  RESULTS
  We validated our support vector machine-based method on several independent datasets. At the same levels of accuracy, our method recovered more experimentally observed physical interactions than a conventional correlation-based approach. Pairs predicted by our method to very likely interact were close in the overall network of interaction, suggesting our method as an aid for functional annotation. We applied the method to predict interactions in yeast (Saccharomyces cerevisiae). A Gene Ontology function annotation analysis and literature search revealed several probable and novel predictions worthy of future experimental validation. We therefore hope our new method will improve the annotation of interactions as one component of multi-source integrated systems.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Columbia University Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA. ts2186@columbia.edu
authors.forename: Ta-Tsen
authors.initials: TT
authors.lastname: Soong
authors.forename: Kazimierz O
authors.initials: KO
authors.lastname: Wrzeszczynski
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D020411:Oligonucleotide Array Sequence Analysis; D011485:Protein Binding; D025941:Protein Interaction Mapping; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*
chemical_list: D029701:Saccharomyces cerevisiae Proteins