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\begin{center}
  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
\end{center}

\begin{center}
  4:15 pm, Tuesday, March 20, 2007 \\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
\end{center}

\begin{center}
  \textsl{Balaji S. Srinivasan} \\
  Departments of Statistics and Computer Science \\
 Stanford University \\
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\begin{center}
  \subsection*{The Statistical Backbone of the Stanford Network Browser}
\end{center}

With hundreds of genomes available and more on the way, the
focus of modern biology has shifted from sequence acquisition to
sequence characterization. The Stanford Network Browser (\verb+networks.stanford.edu+)
is a set of powerful tools for this purpose, used to predict protein asssociations and characterize protein function by laboratories aross the country. 

Here, we begin by discussing the suite of statistical methods
currently available through the Stanford Network Browser: systematic
normalization, data integration, experimental recommendation, and
network alignment. We show that these tools spring from the solution
to a set of supervised learning problems, and then discuss specific
examples in which the Browser has been used to identify unknown
proteins in signaling pathways, cryptic enzymes in metabolic
processes, and missing transporters for cellular nutrients. We
conclude by demonstrating the relationship between the Browser's data
integration algorithm and multi-kernel SVMs, and show that the
integration algorithm can be generalized to produce reference networks
which include other players in the cell besides proteins.


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