BioSpace Collaborative

Academic/Biomedical Research
News & Jobs
Biotechnology and Pharmaceutical Channel Medical Device and Diagnostics Channel Clinical Research Channel BioSpace Collaborative    Job Seekers:  Register | Login          Employers:  Register | Login  

NEWSLETTERS
Free Newsletters
Archive
My Subscriptions

NEWS
News by Subject
News by Disease
News by Date
PLoS
Search News
Post Your News
JoVE

CAREER NETWORK
Job Seeker Login
Most Recent Jobs
Search Jobs
Post Resume
Career Fairs
Career Resources
For Employers

HOTBEDS
Regional News
US & Canada
  Biotech Bay
  Biotech Beach
  Genetown
  Pharm Country
  BioCapital
  BioMidwest
  Bio NC
  BioForest
  Southern Pharm
  BioCanada East
  C2C Services & Suppliers™
Europe
Asia

DIVERSITY

PROFILES
Company Profiles

INTELLIGENCE
Research Store

INDUSTRY EVENTS
Research Events
Post an Event
RESOURCES
Real Estate
Business Opportunities

PLoS By Category | Recent PLoS Articles
Biochemistry - Biophysics - Biotechnology - Computer Science - Physics

WNP: A Novel Algorithm for Gene Products Annotation from Weighted Functional Networks
Published: Thursday, June 28, 2012
Author: Alberto Magi et al.

by Alberto Magi, Lorenzo Tattini, Matteo Benelli, Betti Giusti, Rosanna Abbate, Stefano Ruffo

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.
  More...