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Bioinformatics. 2010 Jul 15;26(14):1759-65. doi: 10.1093/bioinformatics/btq262. Epub 2010 May 27.

Fast integration of heterogeneous data sources for predicting gene function with limited annotation.

Author information

  • 1Department of Computer Science and Center for Cellular and Biomolecular Research, University of Toronto, Canada. smostafavi@cs.toronto.edu

Abstract

MOTIVATION:

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).

AVAILABILITY:

Networks and code are available from: http://morrislab.med.utoronto.ca/sara/SW

PMID:
20507895
PMCID:
PMC2894508
DOI:
10.1093/bioinformatics/btq262
[PubMed - indexed for MEDLINE]
Free PMC Article
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