Annotating proteins by mining protein interaction networks.
BACKGROUND
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%.


BACKGROUND
Code is available upon request. Results and used materials are available online at http://kirac.case.edu/PROTAN.
Kirac
Mustafa
M
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA. kirac@case.edu
Ozsoyoglu
Gultekin
G
Yang
Jiong
J
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003628:Database Management Systems; D030562:Databases, Protein; D004282:Documentation / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008969:Molecular Sequence Data; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000145:classification* / Q000378:metabolism*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods; D015398:Signal Transduction / Q000502:physiology; D018875:Vocabulary, Controlled
D011506:Proteins