Use of clustering to improve performance in fuzzy gene expression analysis

This paper proposes the use of fuzzy modeling algorithms to analyze gene expression data. Current algorithms apply all potential combinations of genes to a fuzzy model of gene interaction (for example, activator/inhibitor/target) and are evaluated on the basis of how well they fit the model. However, the algorithm is computationally intensive; the activator/inhibitor model has an algorithmic complexity of O(N/sup 3/), while more complex models (multiple activators/inhibitors) have even higher complexities. As a result, the algorithm takes a significant amount of time to analyze an entire genome. The purpose of this paper is to propose the use of clustering as a preprocessing method to reduce the total number of gene combinations analyzed. By first analyzing how well cluster centers fit the model, the algorithm can ignore combinations of genes that are unlikely to fit. This will allow the algorithm to run in a shorter amount of time with minimal effect on the results.