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.

[1]  Patrik D'haeseleer,et al.  Linear Modeling of mRNA Expression Levels During CNS Development and Injury , 1998, Pacific Symposium on Biocomputing.

[2]  P. Woolf,et al.  A fuzzy logic approach to analyzing gene expression data. , 2000, Physiological genomics.

[3]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.

[5]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[7]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[8]  Gary D. Stormo,et al.  Modeling Regulatory Networks with Weight Matrices , 1998, Pacific Symposium on Biocomputing.

[9]  J. Barker,et al.  Large-scale temporal gene expression mapping of central nervous system development. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.