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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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\begin{center}
  4:15 p.m., Tuesday, May 23, 2006\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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\begin{center}
  \textsl{   Elizabeth Purdom }\\
Stanford University
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\begin{center}
  \textbf{ Data Analysis using Graphical Structures   } 
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\noindent
In biological experiments, researchers often have  information in the form
of a graph that supplements observed numerical data. Common types of
graphs encountered might be a graph depicting genomic regulatory networks
or a phylogenetic tree depicting the interspecies relationships among the
species of interest.  Incorporating  the information contained in these
graphs into an analysis of the numerical data is an important and non
trivial task. A promising method recently introduced for these kinds of
data-graph problems rely on kernel methods common in the data mining
literature to correlate graphical and numerical data.

\noindent
In this talk, we will show how to explicitly create a regression framework
for these data-graph problems using some kernel methods ideas. In
particular, we will discuss the example of using these techniques for data
connected to phylogenetic trees. We will discuss how in this case the
kernel methods relate to other methods found in phylogenetics and ecology.
We demonstrate these results on a genomic analysis of microbacterial
communities found within the human intestinal track (Eckburg et. al,
Science, 2005).


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