title: Gene Ontology-driven inference of protein-protein interactions using inducers.
abstract: BACKGROUND
  Protein-protein interactions (PPIs) are pivotal for many biological processes and similarity in Gene Ontology (GO) annotation has been found to be one of the strongest indicators for PPI. Most GO-driven algorithms for PPI inference combine machine learning and semantic similarity techniques. We introduce the concept of inducers as a method to integrate both approaches more effectively, leading to superior prediction accuracies.
  
  
  RESULTS
  An inducer (ULCA) in combination with a Random Forest classifier compares favorably to several sequence-based methods, semantic similarity measures and multi-kernel approaches. On a newly created set of high-quality interaction data, the proposed method achieves high cross-species prediction accuracies (Area under the ROC curve ≤ 0.88), rendering it a valuable companion to sequence-based methods.
  
  
  BACKGROUND
  Software and datasets are available at http://bioinformatics.org.au/go2ppi/
  
  
  BACKGROUND
  m.ragan@uq.edu.au.
authors: Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.
authors: Stefan R
authors: SR
authors: Maetschke
authors: Martin
authors: M
authors: Simonsen
authors: Melissa J
authors: MJ
authors: Davis
authors: Mark A
authors: MA
authors: Ragan
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D006801:Humans; D058977:Molecular Sequence Annotation; D060066:Protein Interaction Maps; D011506:Proteins / Q000235:genetics*; D012372:ROC Curve; D012984:Software; D018875:Vocabulary, Controlled; D015003:Yeasts / Q000235:genetics / Q000378:metabolism
chemical_list: D011506:Proteins