59 research outputs found
WNP: A Novel Algorithm for Gene Products Annotation from Weighted Functional Networks
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
Effects of IL-10 and glucose on expression of OPG and RANKL in human periodontal ligament fibroblasts
Modulation of stromal cell-derived factor 1 alpha (SDF-1α) and its receptor CXCR4 in Porphyromonas gingivalis-induced periodontal inflammation
Presence of Porphyromonas gingivalis in esophagus and its association with the clinicopathological characteristics and survival in patients with esophageal cancer
Bycatch fauna of seabob shrimp trawl fisheries from Santa Catarina State, southern Brazil
Transitions in morphologies, fluid regimes, and feeding mechanisms during development of the medusa Lychnorhiza lucerna
- âŠ