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

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

TitleFast integration of heterogeneous data sources for predicting gene function with limited annotation
Publication TypeJournal Article
Year of Publication2010
AuthorsMostafavi, S, Morris, Q
JournalBIOINFORMATICS
Volume26
Pagination1759-1765
Date PublishedJUL 15
Type of ArticleArticle
ISSN1367-4803
AbstractMotivation: 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).
DOI10.1093/bioinformatics/btq262