Gene Ontology-driven inference of protein-protein interactions using inducers

Bioinformatics. 2012 Jan 1;28(1):69-75. doi: 10.1093/bioinformatics/btr610. Epub 2011 Nov 4.


Motivation: 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.

Availability: Software and datasets are available at


Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Databases, Protein
  • Humans
  • Molecular Sequence Annotation*
  • Protein Interaction Maps
  • Proteins / genetics*
  • ROC Curve
  • Software*
  • Vocabulary, Controlled*
  • Yeasts / genetics
  • Yeasts / metabolism


  • Proteins