Work

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

Year: 2010

Type: article

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

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btq262

Authors: Sara Mostafavi, Quaid Morris

Institution: University of Toronto

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,989

Funders: Genome Canada, Ontario Genomics, Ontario Genomics Institute