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                       BIOINFORMATICS COLLOQUIUM
			   College of Science
                        George Mason University
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     A Conditional Entropy Based Approach to Co-clustering for the 
                    Analysis of Gene Expression Data
              
                              Jeff Solka

                        George Mason University

Abstract:
Methods of high-level data exploration capable of robustness in the face of 
noise found within microarray data are few and far between. Solutions making 
use of all original features to derive cluster structure can be misleading 
while those that rely on a trivial feature selection can miss important 
characteristics. We present a method adopted from previous work in the field 
of geography (Guo et al, 2003) relying upon conditional entropy between pairs 
of dimensions to uncover underlying, native cluster structure within a dataset. 
Applied to an artificially clustered data set, this method performed well 
though some sensitivity to multiplicative noise was in evidence. When applied 
to gene expression data, the method produced a clear representation of the 
underlying data structure.