Annotating proteins by mining protein interaction networks.

Mustafa Kirac; Gultekin Ozsoyoglu; Jiong Yang

doi:10.1093/bioinformatics/btl221

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PubMed · 16873481

Annotating proteins by mining protein interaction networks.

Mustafa Kirac·Gultekin Ozsoyoglu·Jiong Yang

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Abstract

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.

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Amino Acid Sequence Artificial Intelligence Database Management Systems Databases, Protein Documentation Information Storage and Retrieval Molecular Sequence Data Natural Language Processing Pattern Recognition, Automated Protein Interaction Mapping Proteins Sequence Alignment Sequence Analysis, Protein Signal Transduction Vocabulary, Controlled Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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Mustafa Kirac, Gultekin Ozsoyoglu, Jiong Yang. 2006-07-15. Annotating proteins by mining protein interaction networks.. https://doi.org/10.1093/bioinformatics%2Fbtl221

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