Prediction of protein functions from protein interaction networks: A Naïve bayes approach

Abstract

Predicting protein functions is one of most challenging problems in bioinformatics. Among several approaches, such as analyzing phylogenetic profiles, homologous protein sequences or gene expression patterns, methods based on protein interaction data are very promising. We propose here a novel method using Naïve Bayes which takes advantage of protein interaction network topology to improve low-recall predictions. Our method is tested on proteins from the Human Protein Reference Database (HPRD) and on the yeast proteins from the BioGRID and compared with other state-of-the-art approaches. Analyses of the results, using several methods that include ROC analyses, indicate that our method predicts protein functions with significantly higher recall without lowering precision. © 2008 Springer Berlin Heidelberg.