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Abstract

\begin{abstract} \section{Motivation:} The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, for example based on \emph{Gene Ontology} (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. \section{Results:} We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and on simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods. \section{Availability:} \href{http://topgo.bioinf.mpi-inf.mpg.de}{topgo.bioinf.mpi-inf.mpg.de} \section{Contact:} \href{alexa@mpi-sb.mpg.de}{alexa@mpi-sb.mpg.de} \end{abstract}