Fast Integration of Heterogeneous Data Sources for Predicting Gene Function with Limited Annotation

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2010 May 29

PMID 20507895

Citations 50

Authors

Sara Mostafavi

Quaid Morris

Affiliations

Soon will be listed here.

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

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