title: Finding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting.
abstract: BACKGROUND
  Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.
  
  
  RESULTS
  We developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at http://virus.hpid.org/interactions.aspx. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at http://virus.hpid.org/PDB_IDs.html.
  
  
  CONCLUSIONS
  When the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.
authors.affiliation: School of Computer Science and Engineering, Inha University, Incheon, South Korea. sujiper@inhainan.net
authors.forename: Jisu
authors.initials: J
authors.lastname: Kim
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.forename: Kyungsook
authors.initials: K
authors.lastname: Han
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism
chemical_list: D011506:Proteins