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Bioinformatics 2006 22(14):e260-e270; doi:10.1093/bioinformatics/btl221
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Annotating proteins by mining protein interaction networks

Mustafa Kirac 1,*, Gultekin Ozsoyoglu 1 and Jiong Yang 1

1 Department of Electrical Engineering and Computer Science, Case Western Reserve University Cleveland, OH, U.S.A.

*To whom correspondence should be addressed.

Motivation: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins.

Results: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%.

Availability: Code is available upon request. Results and used materials are available online at http://kirac.case.edu/PROTAN

Contact: kirac{at}case.edu


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