Graph Algorithms for Integrated Biological Analysis, with Applications to Type 1 Diabetes Data - Clustering Challenges in Biological Network - page 218

method. It is possible to modify the algorithm so that overlap is permitted. This
is a topic of current research within our group. Optimal ways to accomplish this,
however, probably depend on the application. The same may be said for the highly
challenging task of inhomogeneous data integration. We are currently working on
techniques to integrate multiple proteins in a single step, rather than handling them
one at a time. This is not as easy as it might sound, and may require the use of
three rather than just two forms of correlate pairs.

Acknowledgments

We wish to thank an anonymous referee, whose careful review of our original sub-
mission helped us to improve the final presentation. This research has been sup-
ported in part by the National Science Foundation under grant CCR-0311500, by
the National Institutes of Health under grants 1-P01-DA-015027-01, 5-U01-AA-
013512, 1-R01-DK-062103-01 and 1-R01-MH-074460-01, and by the UT-ORNL
Science Alliance. A preliminary version of a portion of this paper was presented
at the DIMACS Workshop on Clustering Problems in Biological Networks, held
at the DIMACS Center at Rutgers University in May, 2006.

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