Protein annotation from protein interaction networks and Gene Ontology

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.

► We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules.
► We explore the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology.
► Our model achieves 51% precision and 60% recall on HPRD database.