title: Protein annotation from protein interaction networks and Gene Ontology.
abstract: We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively.
authors.affiliation: Centre for Diabetes Research, The Western Australian Institute for Medical Research, Australia. cao.nguyen@uwa.edu.au
authors.forename: Cao D
authors.initials: CD
authors.lastname: Nguyen
authors.forename: Katheleen J
authors.initials: KJ
authors.lastname: Gardiner
authors.forename: Krzysztof J
authors.initials: KJ
authors.lastname: Cios
mesh_terms: D001499:Bayes Theorem; D030562:Databases, Protein; D006801:Humans; D058977:Molecular Sequence Annotation; D025941:Protein Interaction Mapping / Q000379:methods; D011506:Proteins / Q000737:chemistry* / Q000235:genetics* / Q000378:metabolism
chemical_list: D011506:Proteins