From: chemistry-request at ccl.net <br />
To: chemistry-request at ccl.net<br />
Date: Sun Jan 22 23:20:53 2006<br />
Subject: 06.05.03 <span class="fancysmall">DIMACS Workshop on Sequence, Structure and Systems Approaches to Predict Protein Function, Rutgers U, NJ</span><br />

<pre>
DIMACS Workshop on Sequence, Structure and Systems Approaches
to Predict Protein Function
May 3 - 5, 2006
DIMACS Center, CoRE Building, Rutgers University
<a href="http://dimacs.rutgers.edu/Workshops/ProteinFunction/announcement.html" target="_blank">http://dimacs.rutgers.edu/Workshops/ProteinFunction/announcement.html</a>

Organizers:
    Anna Panchenko, NIH, panch|mail.nih.gov 
    Teresa Przytycka, NIH, przytyck|mail.nih.gov 
    Mona Singh, Princeton University, mona|CS.Princeton.EDU 

Presented under the auspices of the DIMACS/BioMaPS/MB Center Special Focus 
on Information Processing in Biology.

This special focus is jointly sponsored by the Center for Discrete 
Mathematics and Theoretical Computer Science (DIMACS), the Biological, 
Mathematical, and Physical Sciences Interfaces Institute for Quantitative 
Biology (BioMaPS), and the Rutgers Center for Molecular Biophysics and 
Biophysical Chemistry (MB Center).

While more than two hundred complete genomes have been sequenced, a large 
fraction of genes in genomes do not have assigned functions, and elucidation 
of the biological roles of all unknown gene products remains an elusive 
goal. In addition to genomic sequence data, in recent years we have 
witnessed an explosion of structural data, along with large-scale protein 
network data. This has led to a number of complementary approaches to 
predict protein function based on heterogeneous data from diverse 
experimental sources. The goal of such methods is to decrease the amount 
of time-consuming experimental work necessary to interpret the complexity 
of genomes and proteomes. In general, protein function can be predicted 
by the analysis of specific conserved structural and sequence features, 
by transferring the annotation from experimentally characterized genes 
to their uncharacterized homologs , by genome context and cross-genomic 
analysis, and by analyzing proteins within the context of biological 
networks. Each of these methods faces unique computational and statistical 
challenges.

The goal of the workshop is to bring together biologists, computer 
scientists and mathematicians who work on various aspects of protein 
function prediction. This workshop will provide both a venue for 
reviewing the current state-of-the-art of diverse methods as well as 
a platform for further cross-fertilization and integration of sequence, 
structure and systems approaches.</pre>
</PRE>
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