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

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

Author information

1
Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.

Abstract

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.

PMID:
22057159
DOI:
10.1093/bioinformatics/btr610
[Indexed for MEDLINE]

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