Item

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}