Fast integration of heterogeneous data sources for predicting gene function with limited annotation.
BACKGROUND
Many algorithms that integrate multiple functional association networks for predicting gene function construct a composite network as a weighted sum of the individual networks and then use the composite network to predict gene function. The weight assigned to an individual network represents the usefulness of that network in predicting a given gene function. However, because many categories of gene function have a small number of annotations, the process of assigning these network weights is prone to overfitting.


RESULTS
Here, we address this problem by proposing a novel approach to combining multiple functional association networks. In particular, we present a method where network weights are simultaneously optimized on sets of related function categories. The method is simpler and faster than existing approaches. Further, we show that it produces composite networks with improved function prediction accuracy using five example species (yeast, mouse, fly, Esherichia coli and human).


BACKGROUND
Networks and code are available from: http://morrislab.med.utoronto.ca/sara/SW
Department of Computer Science and Center for Cellular and Biomolecular Research, University of Toronto, Canada. smostafavi@cs.toronto.edu
Sara
S
Mostafavi
Quaid
Q
Morris
D000465:Algorithms; D000818:Animals; D003625:Data Collection; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D005796:Genes / Q000502:physiology*; D023281:Genomics / Q000379:methods*; D006801:Humans; D016014:Linear Models; D051379:Mice