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Updated: May 27, 2026
Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
Published on: March 3, 2015
Gene Ontology-driven inference of protein-protein interactions using inducers
Stefan R Maetschke1, Martin Simonsen, Melissa J Davis
1Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.
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 http://bioinformatics.org.au/go2ppi/
Contact:
m.ragan@uq.edu.au.
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