  Gene regulation is a complex process involving the role of several genomic
elements which work in concert to drive spatio-temporal expression. The
experimental characterization of gene regulatory elements is a very complex and
resource-intensive process. One of the major goals in computational biology is
the \textit{in-silico} annotation of previously uncharacterized elements using
results from the subset of known, previously annotated, regulatory elements.
  The recent results of the ENCODE project (\emph{http://encode.nih.gov})
presented in-depth analysis of such functional (regulatory) non-coding elements
for 1% of the human genome. It is hoped that the results obtained on this
subset can be scaled to the rest of the genome. This is an extremely important
effort which will enable faster dissection of other functional elements in key
biological processes such as disease progression and organ development
(\cite{Kleinjan2005},\cite{Lieb2006}. The computational annotation of these
hitherto uncharacterized regions would require an identification of features
that have good predictive value.
  In this work, we study transcriptional regulation as a problem in
heterogeneous data integration, across sequence, expression and interactome
level attributes. Using the example of the \textit{Gata2} gene and its recently
discovered urogenital enhancers \cite{Khandekar2004} as a case study, we
examine the predictive value of various high throughput functional genomic
assays (from projects like ENCODE and SymAtlas) in characterizing these
enhancers and their regulatory role. Observing results from the application of
modern statistical learning methodologies for each of these data modalities, we
propose a set of features that are most discriminatory to find these enhancers.
