GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function.
BACKGROUND
Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. We propose a new algorithm, GeneMANIA, that is as accurate as the leading methods, while capable of predicting protein function in real-time.


RESULTS
We use a fast heuristic algorithm, derived from ridge regression, to integrate multiple functional association networks and predict gene function from a single process-specific network using label propagation. Our algorithm is efficient enough to be deployed on a modern webserver and is as accurate as, or more so than, the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark; it is robust to redundant and irrelevant data and requires, on average, less than ten seconds of computation time on tasks from these benchmarks.


CONCLUSIONS
GeneMANIA is fast enough to predict gene function on-the-fly while achieving state-of-the-art accuracy. A prototype version of a GeneMANIA-based webserver is available at http://morrislab.med.utoronto.ca/prototype.
Mostafavi
Sara
S
Department of Computer Science, University of Toronto, King's College Road, Toronto, ON, M5S 3G4, Canada.
Ray
Debajyoti
D
Warde-Farley
David
D
Grouios
Chris
C
Morris
Quaid
Q
D000465:Algorithms; D000818:Animals; D003195:Computer Communication Networks; D023281:Genomics; D051379:Mice / Q000235:genetics* / Q000378:metabolism; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D040901:Proteomics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D013997:Time Factors
D011506:Proteins