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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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  4:15 p.m., Tuesday, May 3rd, 2005\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Kanti V Mardia}\\
  Department of Statistics\\
  University of Leeds\\
  England
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  \textbf{STATISTICAL MATCHING AND ALIGNMENT IN PROTEIN BIOINFORMATICS}
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Among the challenges for statistics posed by proteomics are
various alignment and matching problems.  Here we consider matching
protein gels in 2 dimensions,and aligning active sites of proteins in 3
dimensions.  In the latter case, we also want to use information
related to the grouping of the amino acids.  We introduce hierarchical
Bayesian models for matching configurations of points in space, where
the points are either unlabelled, or have at most a partial labelling
constraining the matching, and in which some points may only appear in
one of the configurations.  We derive procedures for simultaneous
inference about the matching and the transformation, using a Bayesian
approach (Green and Mardia, 2004).  Our procedure is compared to other
methods as a graph theoretic method (Gold, 2003) and EM algorithm
(Taylor, Mardia and Kent, 2003,Kent ,Mardia and Taylor,2004). Its 
connection with the work of Wu ,Schmidler,Hastie and Burtlag (1998)
will be discussed.

Implementation and performance of these methods on the proteomic tasks
is described, and we discuss some open problems and suggest directions
for future work.

\section*{REFERENCES}

Eidhammer, T., Jonassen, T. and Taylor, W.R. (2004).  Protein
Bioinformatics.  Wiley, Chichester.

Gold, N.D., Pickering, S.J. and Westhead, D.R. (2003).  Predicting
protein function from structure using SITEDB: evaluation of a method
based on functional site- similarity.  Preprint.

Green, P.J. and Mardia, K.V. (2004).  Bayesian alignment using
hierarchical models with applications in protein bioinformatics.
\url{http://www.stats.bris.ac.uk/~peter/Research.html#Complex}

Kent, J.T. , Mardia, K.V. and Taylor, C.C. (2004).Matching problems for
unlabelled configurations .LASR 04 Proceedings of Bioinformatics,
Images ,and Wavelets, 33-40. Edited by R.G.  Aykroyd,and S.Barber and
K.V. Mardia.  Leeds University Press.

Taylor, C.C., Mardia, K.V. and Kent, J.T. (2003).  Matching unlabelled
configurations using the EM algorithm.LASR 03 Proceedings of Stochastic
Geometry, Biological Structure and Images, 19-21. Edited by R.G.
Aykroyd, K.V. Mardia and M.J. Langdon.  Leeds University Press.

Wu,T.D.Wu, T.D., Schmidler, S.C., Hastie, T. and Brutlag, G. (1998).
Regression analysis of multiple protein structures. Journal of
Computational Biology, , pp 585--595. 

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