[colloquium] PICASso Talk Reminder: Prediction of protein function viagraph-theoretic analysis of interaction maps

[Sandy Barbu]

TALK ANNOUNCEMENT:
------------------
 TITLE:  	Prediction of protein function via graph-theoretic
             analysis of interaction maps
 SPEAKER: 	Elena Nabieva
 		Department of Computer Science
 TIME:	Wednesday, October 12, 2005
 LOCATION:	Room 402, Computer Science Building

 ABSTRACT:

Determining protein function is one of the most important problems in the
post-genomic era. For the typical proteome, there are no functional
annotations for one-third or more of its proteins. Traditionally,
computational methods to assign protein function have relied largely on
sequence homology.  Recently, the emergence of high-throughput experimental
data sets led to a number of alternative, non-homology based methods for
functional annotations. In particular, recent high-throughput experiments
have determined proteome-scale protein physical interaction maps for several
organisms. These physical interactions are complemented by an abundance of
data about other types of functional relationships between proteins,
including genetic interactions, knowledge about co-expression and shared
evolutionary history. Taken together, these pairwise linkages can be used to
build whole-proteome protein interaction maps.

We have developed a network-flow based algorithm, FunctionalFlow, that
exploits the underlying structure of protein interaction maps in order to
predict protein function. In cross-validation testing on the yeast proteome,
we show that FunctionalFlow has improved performance over previous methods
in predicting the function of proteins with few (or no) annotated protein
neighbors. By comparing several methods that use protein interaction maps to
predict protein function, we demonstrate that FunctionalFlow performs well
because it takes advantage of both network topology and some measure of
locality. Finally, we show that performance can be improved substantially as
we consider multiple data sources and use them to create weighted
interaction networks.

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