Journal
BIOINFORMATICS
Volume 28, Issue 1, Pages 69-75Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btr610
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Funding
- Australian Research Council Centre of Excellence in Bioinformatics [DP110103384, CE034822]
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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.
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