WNP: A Novel Algorithm for Gene Products Annotation from Weighted Functional Networks

• Published in: PloS one Vol. 7; no. 6; p. e38767
• Main Authors: Magi, Alberto, Tattini, Lorenzo, Benelli, Matteo, Giusti, Betti, Abbate, Rosanna, Ruffo, Stefano
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.06.2012; Public Library of Science (PLoS)
• Subjects: Algorithms; Annotations; Arabidopsis; Arabidopsis - genetics; Arabidopsis Proteins - classification; Arabidopsis Proteins - genetics; Arabidopsis Proteins - metabolism; Arabidopsis thaliana; Baking yeast; Bayes Theorem; Bayesian analysis; Biology; Cellulose; Computation; Computational Biology; Computer applications; Computer Science; Computer simulation; Data integration; Engineering; Experimental methods; Experiments; Genes; Genomics; Hypoxia; Metabolism; Methods; Molecular Sequence Annotation; Networks; Nitrates; Phosphorylation; Physics; Plasma; Predictions; Protein Interaction Mapping; Proteins; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae Proteins - classification; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; Transcription factors; Yeast; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0038767

Predicting the biological function of all the genes of an organism is one of the fundamental goals of computational system biology. In the last decade, high-throughput experimental methods for studying the functional interactions between gene products (GPs) have been combined with computational approaches based on Bayesian networks for data integration. The result of these computational approaches is an interaction network with weighted links representing connectivity likelihood between two functionally related GPs. The weighted network generated by these computational approaches can be used to predict annotations for functionally uncharacterized GPs. Here we introduce Weighted Network Predictor (WNP), a novel algorithm for function prediction of biologically uncharacterized GPs. Tests conducted on simulated data show that WNP outperforms other 5 state-of-the-art methods in terms of both specificity and sensitivity and that it is able to better exploit and propagate the functional and topological information of the network. We apply our method to Saccharomyces cerevisiae yeast and Arabidopsis thaliana networks and we predict Gene Ontology function for about 500 and 10000 uncharacterized GPs respectively.