Interactive Analysis of Gene Interactions Using Graphical gaussian model

DNA microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that show similar expression patterns. However, this approach usually fails to unveil gene-gene interactions in the same cluster. Association rule mining and loglinear models have been used for this purpose, but their inherent limitations as well as information loss due to discretization limit the applicability of the results. Here we propose the use of a Graphical Gaussian Model to discover pairwise gene interactions. We have constructed a prototype system that permits rapid interactive exploration of gene relationships; results can be validated by experts or known information, or suggest new experiments. We have tested our methodology using the yeast microarray data. Our results reveal some previously unknown interactions that have solid biological explanations.

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