GenExplore: Interactive Exploration of Gene Interactions  

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Microarray data 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 multiple interactions by the same gene. In this project we aim to apply statistical models (Graphical Gaussian Models, Graphical Loglinear Models) and data mining techniques (Hierarchical Clustering, Association Rules) to discover gene interactions (pairwise and multi-way levels). We aim to construct a prototype system that permits rapid interactive exploration of gene relationships; results can be validated by experts or domain information, or suggest new experiments. 

Research Tasks


     Faculty member

      Graduate Student




GenExplore 0.98 beta version is available.

Related Links

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